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Understanding Speech in Noise: Hearing Loss and/or a Listening Problem?

by Dr. Douglas L. Beck Au.D. Vice President of Academic Sciences Oticon Inc. , Somerset, NJ

The most common hearing tests present simple sounds (pure tones) at different pitches and at different loudness levels. Typically, the listener’s goal is to press a button or raise a hand when they “hear” the tone. As such, that simple pure tone hearing test evaluates how loud a sound must be, across various pitches, to be perceived. However, simply perceiving speech sounds is not enough to understand speech sounds. Not only must we perceive speech sounds, but our auditory system and our brain must be able to make sense of the speech sounds we perceive. For people to make sense of speech sounds, or to understand speech in noise (such as restaurants and cocktail parties) the primary speech sounds of interest need to be significantly louder than the secondary background speech sounds. The loudness difference between the primary speech sound and the secondary background sound is called the signal-to-noise ratio (SNR).

In general, when the primary speaker is a little louder than the secondary speaker, most people can listen clearly to the primary speaker. However, in the cocktail party situation, the secondary speakers are actually equally loud, or louder than, the primary speaker, creating a dis-advantageous SNR. When this happens, the listener might report they can “hear” but they cannot “understand.” This is a very common observation.

As we age, it is common for people to lose hearing ability in the higher speech pitches. However, even when we amplify sounds such that the higher pitches can be heard, our auditory systems and our brains must be able to make sense of them - and to a large degree, that depends on improving the SNR. Simply hearing the sounds is not enough. The sounds we want to listen to must be substantially louder than the background sounds for our auditory system and our brain to process them correctly. Further, as hearing loss increases, the SNR must also increase in order to make sense of the perceived sounds.

One reason people become frustrated with simple amplification devices is that many of these devices can only make sound louder, they cannot improve the SNR. Specifically, many older or basic devices lack the processing power found in today’s modern hearing aid products that improve the SNR. As a result, the primary speech sounds and the secondary background sounds are louder, but not necessarily clearer, because the SNR (which was inadequate to begin with) remains inadequate; thereby facilitating a loud and unclear signal, which is arguably (for many people) worse than simply not hearing.

Technology advancements in hearing aids can allow the use of multiple processing systems to not only make sounds louder, but to improve the SNR. Technologies which help accomplish these goals include: directional microphones; beam-forming microphones; multi-speaker access technology (MSAT); T-coils; Bluetooth streaming for music and telephone use; as well as very substantial SNR improvement tools such as FM and Digital Remote Microphones (DRM) which use radio waves to send the signal from a tiny microphone worn by the person speaking, directly to the person wearing hearing aids (up to 40 feet away) at an excellent and easy-to-listen-to SNR.

The ability to better understand speech in noise and to enhance listening (using today’s advanced hearing aid technology) has significantly improved in the last 3 to 5 years. I recommend that when considering hearing aid amplification, ask your hearing care professional to measure your speech-in-noise ability unaided (with just your own ears) and again with hearing aids. I think most people will be pleasantly surprised with the difference today’s technology can deliver.

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FIGURE 1. Subject and speaker diagram where X marks the placement of the calibration microphone.

To perform free-field calibration, the microphone location must estimate the center of the subject’s head position (marked by an X in Figure 1). All measurements should be made with the microphone in this static position. Set the sound level meter (SLM) to SPL mode (see FIGURE 2).

• Subject and speaker diagram where X marks the calibration microphone (FIGURE 1).
• Speakers at 0 degrees (right channel) and 180 degrees (left channel).
• MedRx free-field calibrations (completed by certified technician).
• Complete full pure-tone, free-field calibration using warble tones.
• MedRx equipment must have white noise, speech babble (A Weighted), speech-tone calibrated (1000 Hz cal. tone).
• Save calibration.

FIGURE 2. Avant advanced audiometry settings, set free field to SPL.

Special Thanks

The authors offer thanks and appreciation to Andy Vermiglio, AuD, and Caleb Sparkman, AuD, for their review and valuable input regarding the preparation of this manuscript. 

Beck DL. (2017) Best practices in hearing aid dispensing: An interview with Michael Valente, PhD. Hear Rev 24(12):39–41.

Beck DL, Danhauer JL, Abrams HB, Atcherson SR, Brown DK, Chasin M, Clark JG, De Placido C, Edwards B, Fabry DA, Flexer C, Fligor B, Frazer G, Galster JA, Gifford L, Johnson CE, Madell J, Moore DR, Roeser RJ, Saunders GH, Searchfield GD, Spankovich C, Valente M, Wolfe J. (2018) Audiologic considerations for people with normal hearing sensitivity yet hearing difficulty and/or speech-in-noise problems. Hear Rev 25(10):28–38.

Beck DL, Ng E, Jensen JJ. (2019): A scoping review 2019: OpenSound Navigator. Hear Rev 26(2):28–31.

Clark, JG, Huff C, Earl B. (2017) Clinical practice report card–Are we meeting best practice standards for adult hearing rehabilitation? Audiol Today 29(6):15–25.

Carhart R. (1946) Tests for selection of hearing aids. Laryngoscope 56(12):780–794.

Dillon H. (2012) Hearing Aids. (2d Ed) Thieme Publishers. 

The Harvard Report. (1946) Davis H, Hudgins CV, Marquis RJ, et al. The Selection of Hearing Aids. Laryngoscope 56(3):85–115.

Jerger J. (2018) Lessons from the past: Two influential articles in the early history of audiology. Hear Rev . Published Dec 5.

Killion MC. (2002) New thinking on hearing in noise: a generalized articulation index. Sem Hear 23(1):57–75.

Lawson G. (2012) Speech Audiometry, Word Recognition, and Binomial Variables: Interview with Gary Lawson. www.audiology.org/news/speech-audiometry-word-recognition-and-binomial-variables-interview-gary-lawson-phd.

Loven F, Hawkins D. (1983) Interlist equivalency of the CID W-22 word lists presented in quiet and in noise. Ear Hear 4:91–97.

Stockley KB, Green WB. (2000) Interlist equivalency of the Northwestern University auditory test No. 6 in quiet and noise with adult hearing-impaired individuals. J Am Acad Audiol 11:91–96.

Taylor B, Mueller G. (2017) Fitting and Dispensing Hearing Aids (2d ed) Plural Publishing.

Vermiglio AJ, Herring CC, Heeke P, Post CE, Fang X. (2019) Sentence recognition in steady-state speech-shaped noise versus four-talker babble. J Am Acad Audiol 30(1):54–65. 

Vermiglio AJ, Soli SD, Freed DJ, Fisher LM. (2012) The relationship between high-frequency pure-tone hearing loss, hearing in noise test (HINT) thresholds, and the articulation index. J Am Acad Audiol 23(10):779–788.

Wilson RH. (2011) Clinical experience with the words-in-noise test on 3430veterans: comparisons with pure-tone thresholds and word recognition in quiet. J Am Acad Audiol 22(7):405–423.

Douglas L. Beck

Douglas L. Beck, AuD, is the executive director of Academic Sciences at Oticon, Inc, in Somerset, New Jersey.

Lauren Benitez

Lauren Benitez, AuD, is the hearing clinic director at MedRx, in Largo, Florida.

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• Using Speech-in-Noise Tests to Make Better Hearing Aid Selection Decisions

Brian Taylor, AuD

• Hearing Aids - Adults

This is an edited transcript of the live expert e-seminar presented on 1/24/11. Register here to view the recorded course. Selecting the ideal hearing aid for a patient is a blend of art and science. While a basic pure-tone configuration may have once steered our decision making, real-world speech tests have now taken center stage as an objective measuring tool with which to select amplification. Speech-in-noise (SIN) testing for some may be a very routine practice, and for others may seem a bit daunting because it is an unknown. This article will attempt to familiarize either audiologist with the underlying fundamentals of conducting these tests clinically and how to use that information to make better hearing-aid-selection decisions. It is well understood that to use and continue use of a test, we have to understand how it fits into our clinical practice and how we can get the most bang for our buck when we invest both our time and resources. Why Conduct SIN Tests? There are many good reasons as to why SIN testing can be very beneficial when used routinely in the clinic. One thing we know is that SIN tests can directly address the most common complaint that patients have, which is an inability to hear well in background noise. Because it is a common complaint in all age ranges, the results we get on SIN tests can provide some very valuable insights into what might be the most appropriate amplification strategy. The results of these tests may indicate quite clearly if someone needs directional microphones, stronger noise reduction programming, extra signal processing to try to manage the background noise, or that they are in fact doing so well that we do not need to emphasize these things at all. Perhaps most importantly, it gives us more precision in the way we counsel patients about realistic expectations. There is logical as well as emotional reasoning for conducting SIN tests in the clinic. Let's look at some of the logical reasons first. MarkeTrak VIII (Kochkin, 2010) shows some ubiquitous, yet very valuable, information. The survey indicates that the number of patients who are extremely dissatisfied with their hearing aid in noisy situations is 14%, and those that love it in noise are only at 11%, with the other 75% falling somewhere in between. These are all patients with technology that is less than four years old. The breakdown of some of the "noisy situations" was as follows: Restaurants- 14% dissatisfied, 18% very satisfied; Sports events- 11% dissatisfied, 18% very satisfied; School and classroom where noise can be a huge problem- 10% are dissatisfied and only 18% are very satisfied. The take home point is that there is not a big difference between those that love and those that hate their amplification in common noisy environments. Because patients continue to be tremendously dissatisfied in some of the noisier background situations, as clinicians we need to think of ways to more carefully identify these problems during the pre-fit and manage them with amplification strategies. The same MarkeTrak survey (Kochkin, 2010) also looked at reasons why good hearing aid candidates ended up putting their hearing aids in the drawer. The overwhelming number-one reason was the inability of the hearing aids to perform well in noise. From a logical perspective, when patients are struggling in noise and not adopting hearing aids because of background noise, it makes sense to have a reliable test to measure someone's ability or inability to hear in those challenging situations that lead to dissatisfaction and non use. There is also an emotional aspect to conducting SIN tests routinely in the clinic. There is actually a little bit of data around this, as well. Carole Rogin (2009) discussed a study conducted by the Better Hearing Institute that examined the consumer's journey through the hearing aid testing and purchasing process, as well as the reasons people reported for being delighted with their hearing aids. From this study, Rogin concluded that in order to delight patients, professionals need to provide high tech, high-touch service delivery. High tech would include SIN testing as it actually tests something that patients are encountering on a daily basis, and that contributes to a better, more thorough evaluation. The whole concept of high tech high-touch is a great way to drive what Rogin (2009) calls hearing aid delight. Additionally, hearing aid delight might simply be that patients that are so enthralled with the way you are delivering services that they will spread the message through word of mouth to generate even more business. So from purely a business standpoint it makes sense to use SIN testing because it does contribute to that high-touch high tech service delivery. The Fundamentals Behind Hearing in Noise Hearing loss can be generally categorized into two types: loss of audibility and loss of clarity. We know from basic hearing science that the loss of audibility, or volume, can be attributed to damage of the outer hair cells. We also know that there is a fairly predictable relationship between the thresholds and the amount of gain a patient needs to restore audibility. Loss of clarity, on the other hand, is attributed to damage of the inner hair cells or central auditory nervous system. We also know that, for the most part, there is a pretty unpredictable relationship between the audiometric thresholds and that loss of clarity. Loss of clarity is distortion-based and is not remedied by additional gain or volume. It can, however, be quantified with SIN testing that directly measures something called signal to noise ratio loss (SNR loss). Because you cannot completely get insight into a patient's difficulty based on pure tones or word recognition scores alone, SIN tests were developed to create more real-world listening scenarios and evaluate a person's aided performance against a normal performance-intensity function. SIN tests help you decipher and quantify how much distortional loss there might be. Another key point to consider when we are testing is the fact that language is redundant. There are actually two types of redundancy: extrinsic and intrinsic. Extrinsic redundancy is being able to use rules of language such as syntax and grammar to fill in missing blanks. For, example, because you have grown up and innately know English, if you miss a couple of words you can often times fill in the gaps because of that innate knowledge of your language. This kind of redundancy is a key compensatory strategy as we age. Intrinsic, or internal, redundancy is the auditory system's ability to carry the message to the language centers of the brain. This is also something highly affected in an aging system. This leads to some very important clinical questions: how does aging affect speech perception in noise? How does it affect internal redundancies? And how does central auditory processing affect speech perception in noise? An interesting study by Wong and colleagues at Northwestern University (2010) compared QuickSIN scores and the MRI of the prefrontal cortex on a group of 15 older adults to a group of 14 younger adults. The results suggested a decline of volume in thickness of the prefrontal cortex as a result of aging, which contributed to a declining ability to perceive speech in noise. What they also found is that the larger frontal cortex volume actually compensated for declining peripheral hearing. It is interesting to think that somebody who has a larger prefrontal cortex volume may, in the face of aging, actually perform better in noise. Another review by Akeroyd (2008) in the U.K. compared 20 studies over a 20-year period and examined the relationship between speech perception in noise and central auditory processing factors. Nineteen of the twenty studies indicated a fairly strong relationship between speech perception and central auditory processing factors. One of the main conclusions was that even though hearing loss may occur peripherally, hearing loss remains the primary predictor of speech perception in noise, although there is a strong secondary affect from cognition and central auditory processing. In light of these findings, we should strongly consider the relationship between central auditory processing, aging, and speech perception in noise, and make sure our SIN testing reflects those challenges accordingly. What Tests Should We Be Using In the Clinic? Luckily, there is a choice when it comes to a variety of testing materials. There are two different methodologies when it comes to evaluating the auditory processing system: top down or bottom up. Bottom-up processing simply means that you are evaluating more of those lower-level processes such as audibility and perhaps intelligibility. You are going to use words to measure that. Top-down processing would be a measurement of the entire system including memory, comprehension and intelligibility. Likely, you would want to use sentences for this. Of course, there are pros and cons associated with each. If you are a top-down kind of a person using sentences, you must take into account that memory and other central auditory processing mechanisms might influence your results. This is an acceptable approach when a patient scores poorly on a SIN test, because it does not matter necessarily if it is a central problem or peripheral problem. You know that person is going to struggle in background noise with amplification based on your SIN results. On the other side of things, you might want to get a more precise idea of what is going on with the entire system. Therefore, you might be more of a bottom-up person where you measure words in noise and then maybe some other central auditory processing test to measure that system. There is no "right" methodology to use. The answer really depends on what you are trying to accomplish in your clinic and how much time you have to spend with each patient. The good news is there are tests available for both bottom-up and top-down people. What Are We Trying to Evaluate? When we ask that question to ourselves, there are a few things to keep in mind. Most of us have very limited time in our busy clinic, and the time should be valuable to both you and the patient. Whatever test we use on the patient has to have high face validity. The patient has to understand what it is you are trying to measure, because that will probably result in a solid recommendation or treatment plan that the patient understands, which ends up being more cost efficient for both parties. There are four things that we know contribute to a more successful outcome, or maybe on the flip side, derail a fitting and lead to an unsuccessful outcome. Those would be speech intelligibility in everyday listening, annoyance from background noise, comfort in noise, and tolerance of loud sounds. For the purposes of our discussion, we will focus on speech intelligibility in everyday listening (or noise) and annoyance from noise. Speech perception testing is always performed suprathreshold in many different arrangements to evaluate different variables. We can look at intelligibility versus comfort, annoyance or tolerance, or actual performance in quiet versus noise, fixed versus adaptive, and sentences versus words. Let's look at the whole idea of speech in quiet versus speech in noise. Some tremendous work has been completed out of the VA by Richard Wilson and Rachel McArdle. In a 2005 study, Wilson and McArdle were comparing word recognition percent-correct scores in quiet versus word recognition scores in noise as SNR loss. They concluded that a majority of the participants did, in fact, score well in quiet. Of the group who heard particularly well in quiet, only some of the subject group scored below 5 dB SNR loss (meaning better performance) on the QuickSIN test (Etymotic Research, 2001; Killion et al., 2004). A larger majority of the subject group scored higher (poorer) on the QuickSIN with SNR losses of greater than 10 to 12, indicating tremendous difficulty in background noise. So both groups had at least an 80%-correct word recognition score in quiet with a wide range of scores in noise. This easily reminds us that good word recognition in quiet does not automatically indicate good word recognition in noise. The obvious conclusion would be that you really need to be measuring speech recognition in quiet and speech recognition in noise separately. So what are some of the options that are available to you? Some of the objective intelligibility tests which feature adaptive signal-to-noise ratios (SNR) are:

• Hearing in Noise Test (HINT) (Nilsson, Soli, and Sullivan, 1994);
• Words in Noise (WIN) (Wilson, 2003; Wilson and Burks, 2005);
• QuickSIN (Etymotic Research, 2001; Killion et al., 2004),
• Bamford-Kowal-Bench SIN (BKB-SIN) (Etymotic Research, 2005; Bench, Kowal, and Bamford, 1979; Niquette et al., 2003).
• Connected Speech Test (CST) (Cox, Alexander, and Gilmore, 1987);
• Speech Perception in Noise Test (SPIN) (Kalikow, Stevens, and Elliott, 1977).

Director of Practice Development & Clinical Affairs

Brian Taylor is the Director of Practice Development & Clinical Affairs for Unitron. He is also the Editor of Audiology Practices, the quarterly publication of the Academy of Doctor’s of Audiology. During the first decade of his career, he practiced clinical audiology in both medical and retail settings. Since 2003, Dr. Taylor has held a variety of management positions within the industry in both the United States and Europe. He has published over 30 articles and book chapters on topics related to hearing aids, diagnostic audiology and business management. Brian is the co-author, along with Gus Mueller, of the text book Fitting and Dispensing Hearing Aids, published by Plural, Inc. He holds a Master’s degree in audiology from the University of Massachusetts and a doctorate in audiology from Central Michigan University.   Brian Taylor is the Director of Practice Development & Clinical Affairs for Unitron. He is also the Editor of Audiology Practices.

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Training in Speech Audiometry

• Why Perform Functional Hearing Tests?

Speech Audiometry: An Introduction

Description, table of contents, what is speech audiometry, why perform speech audiometry.

• Contraindications and considerations

Audiometers that can perform speech audiometry

How to perform speech audiometry, results interpretation, calibration for speech audiometry.

Speech audiometry is an umbrella term used to describe a collection of audiometric tests using speech as the stimulus. You can perform speech audiometry by presenting speech to the subject in both quiet and in the presence of noise (e.g. speech babble or speech noise). The latter is speech-in-noise testing and is beyond the scope of this article.

Speech audiometry is a core test in the audiologist’s test battery because pure tone audiometry (the primary test of hearing sensitivity) is a limited predictor of a person’s ability to recognize speech. Improving an individual’s access to speech sounds is often the main motivation for fitting them with a hearing aid. Therefore, it is important to understand how a person with hearing loss recognizes or discriminates speech before fitting them with amplification, and speech audiometry provides a method of doing this.

A decrease in hearing sensitivity, as measured by pure tone audiometry, results in greater difficulty understanding speech. However, the literature also shows that two individuals of the same age with similar audiograms can have quite different speech recognition scores. Therefore, by performing speech audiometry, an audiologist can determine how well a person can access speech information.

Acquiring this information is key in the diagnostic process. For instance, it can assist in differentiating between different types of hearing loss. You can also use information from speech audiometry in the (re)habilitation process. For example, the results can guide you toward the appropriate amplification technology, such as directional microphones or remote microphone devices. Speech audiometry can also provide the audiologist with a prediction of how well a subject will hear with their new hearing aids. You can use this information to set realistic expectations and help with other aspects of the counseling process.

Below are some more examples of how you can use the results obtained from speech testing.

Identify need for further testing

Based on the results from speech recognition testing, it may be appropriate to perform further testing to get more information on the nature of the hearing loss. An example could be to perform a TEN test to detect a dead region or to perform the Audible Contrast Threshold (ACT™) test .

Inform amplification decisions

You can use the results from speech audiometry to determine whether binaural amplification is the most appropriate fitting approach or if you should consider alternatives such as CROS aids.

You can use the results obtained through speech audiometry to discuss and manage the amplification expectations of patients and their communication partners.

Unexpected asymmetric speech discrimination, significant roll-over , or particularly poor speech discrimination may warrant further investigation by a medical professional.

Non-organic hearing loss

You can use speech testing to cross-check the results from pure tone audiometry for suspected non‑organic hearing loss.

Contraindications and considerations when performing speech audiometry

Before speech audiometry, it is important that you perform pure tone audiometry and otoscopy. Results from these procedures can reveal contraindications to performing speech audiometry.

Otoscopic findings

Speech testing using headphones or inserts is generally contraindicated when the ear canal is occluded with:

• Foreign body
• Or infective otitis externa

In these situations, you can perform bone conduction speech testing or sound field testing.

Audiometric findings

Speech audiometry can be challenging to perform in subjects with severe-to-profound hearing losses as well as asymmetrical hearing losses where the level of stimulation and/or masking noise  required is beyond the limits of the audiometer or the patient's uncomfortable loudness levels (ULLs).

Subject variables

Depending on the age or language ability of the subject, complex words may not be suitable. This is particularly true for young children and adults with learning disabilities or other complex presentations such as dementia and reduced cognitive function.

You should also perform speech audiometry in a language which is native to your patient. Speech recognition testing may not be suitable for patients with expressive speech difficulties. However, in these situations, speech detection testing should be possible.

Before we discuss speech audiometry in more detail, let’s briefly consider the instrumentation to deliver the speech stimuli. As speech audiometry plays a significant role in diagnostic audiometry, many audiometers include – or have the option to include – speech testing capabilities.

Table 1 outlines which audiometers from Interacoustics can perform speech audiometry.

Table 1: Audiometers from Interacoustics that can perform speech audiometry.

Because speech audiometry uses speech as the stimulus and languages are different across the globe, the way in which speech audiometry is implemented varies depending on the country where the test is being performed. For the purposes of this article, we will start with addressing how to measure speech in quiet using the international organization of standards ISO 8252-3:2022 as the reference to describe the terminology and processes encompassing speech audiometry. We will describe two tests: speech detection testing and speech recognition testing.

Speech detection testing

In speech detection testing, you ask the subject to identify when they hear speech (not necessarily understand). It is the most basic form of speech testing because understanding is not required. However, it is not commonly performed. In this test, words are normally presented to the ear(s) through headphones (monaural or binaural testing) or through a loudspeaker (binaural testing).

Speech detection threshold (SDT)

Here, the tester will present speech at varying intensity levels and the patient identifies when they can detect speech. The goal is to identify the level at which the patient detects speech in 50% of the trials. This is the speech detection threshold. It is important not to confuse this with the speech discrimination threshold. The speech discrimination threshold looks at a person’s ability to recognize speech and we will explain it later in this article.

The speech detection threshold has been found to correlate well with the pure tone average, which is calculated from pure tone audiometry. Because of this, the main application of speech detection testing in the clinical setting is confirmation of the audiogram.

Speech recognition testing

In speech recognition testing, also known as speech discrimination testing, the subject must not only detect the speech, but also correctly recognize the word or words presented. This is the most popular form of speech testing and provides insights into how a person with hearing loss can discriminate speech in ideal conditions.

Across the globe, the methods of obtaining this information are different and this often leads to confusion about speech recognition testing. Despite there being differences in the way speech recognition testing is performed, there are some core calculations and test parameters which are used globally.

Speech recognition testing: Calculations

There are two main calculations in speech recognition testing.

1. Speech recognition threshold (SRT)

This is the level in dB HL at which the patient recognizes 50% of the test material correctly. This level will differ depending on the test material used. Some references describe the SRT as the speech discrimination threshold or SDT. This can be confusing because the acronym SDT belongs to the speech detection threshold. For this reason, we will not use the term discrimination but instead continue with the term speech recognition threshold.

2. Word recognition score (WRS)

In word recognition testing, you present a list of phonetically balanced words to the subject at a single intensity and ask them to repeat the words they hear. You score if the patient repeats these words correctly or incorrectly.  This score, expressed as a percentage of correct words, is calculated by dividing the number of words correctly identified by the total number of words presented.

In some countries, multiple word recognition scores are recorded at various intensities and plotted on a graph. In other countries, a single word recognition score is performed using a level based on the SRT (usually presented 20 to 40 dB louder than the SRT).

Speech recognition testing: Parameters

Before completing a speech recognition test, there are several parameters to consider.

1. Test transducer

You can perform speech recognition testing using air conduction, bone conduction, and speakers in a sound-field setup.

2. Types of words

Speech recognition testing can be performed using a variety of different words or sentences. Some countries use monosyllabic words such as ‘boat’ or ‘cat’ whereas other countries prefer to use spondee words such as ‘baseball’ or ‘cowboy’. These words are then combined with other words to create a phonetically balanced list of words called a word list.

3. Number of words

The number of words in a word list can impact the score. If there are too few words in the list, then there is a risk that not enough data points are acquired to accurately calculate the word recognition score. However, too many words may lead to increased test times and patient fatigue. Word lists often consist of 10 to 25 words.

You can either score words as whole words or by the number of phonemes they contain.

An example of scoring can be illustrated by the word ‘boat’. When scoring using whole words, anything other than the word ‘boat’ would result in an incorrect score.

However, in phoneme scoring, the word ‘boat’ is broken down into its individual phonemes: /b/, /oa/, and /t/. Each phoneme is then scored as a point, meaning that the word boat has a maximum score of 3. An example could be that a patient mishears the word ‘boat’ and reports the word to be ‘float’. With phoneme scoring, 2 points would be awarded for this answer whereas in word scoring, the word float would be marked as incorrect.

5. Delivery of material

Modern audiometers have the functionality of storing word lists digitally onto the hardware of the device so that you can deliver a calibrated speech signal the same way each time you test a patient. This is different from the older methods of testing using live voice or a CD recording of the speech material. Using digitally stored and calibrated speech material in .wav files provides the most reliable and repeatable results as the delivery of the speech is not influenced by the tester.

6. Aided or unaided

You can perform speech recognition testing either aided or unaided. When performing aided measurements, the stimulus is usually played through a loudspeaker and the test is recorded binaurally.

Global examples of how speech recognition testing is performed and reported

Below are examples of how speech recognition testing is performed in the US and the UK. This will show how speech testing varies across the globe.

Speech recognition testing in the US: Speech tables

In the US, the SRT and WRS are usually performed as two separate tests using different word lists for each test. The results are displayed in tables called speech tables.

The SRT is the first speech test which is performed and typically uses spondee words (a word with two equally stressed syllables, such as ‘hotdog’) as the stimulus. During this test, you present spondee words to the patient at different intensities and a bracketing technique establishes the threshold at where the patient correctly identifies 50% of the words.

In the below video, we can see how an SRT is performed using spondee words.

Below, you can see a table showing the results from an SRT test (Figure 1). Here, we can see that the SRT has been measured in each ear. The table shows the intensity at which the SRT was found as well as the transducer, word list, and the level at which masking noise was presented (if applicable). Here we see an unaided SRT of 30 dB HL in both the left and right ears.

Once you have established the intensity of the SRT in dB HL, you can use it to calculate the intensity to present the next list of words to measure the WRS. In WRS testing, it is common to start at an intensity of between 20 dB and 40 dB louder than the speech recognition threshold and to use a different word list from the SRT. The word lists most commonly used in the US for WRS are the NU-6 and CID-W22 word lists.

In word recognition score testing, you present an entire word list to the test subject at a single intensity and score each word based on whether the subject can correctly repeat it or not. The results are reported as a percentage.

The video below demonstrates how to perform the word recognition score.

Below is an image of a speech table showing the word recognition score in the left ear using the NU‑6 word list at an intensity of 55 dB HL (Figure 2). Here we can see that the patient in this example scored 90%, indicating good speech recognition at moderate intensities.

Speech recognition testing in the UK: Speech audiogram

In the UK, speech recognition testing is performed with the goal of obtaining a speech audiogram. A speech audiogram is a graphical representation of how well an individual can discriminate speech across a variety of intensities (Figure 3).

In the UK, the most common method of recording a speech audiogram is to present several different word lists to the subject at varying intensities and calculate multiple word recognition scores. The AB (Arthur Boothroyd) word lists are the most used lists. The initial list is presented around 20 to 30 dB sensation level with subsequent lists performed at quieter intensities before finally increasing the sensation level to determine how well the patient can recognize words at louder intensities.

The speech audiogram is made up of plotting the WRS at each intensity on a graph displaying word recognition score in % as a function of intensity in dB HL. The following video explains how it is performed.

Below is an image of a completed speech audiogram (Figure 4). There are several components.

Point A on the graph shows the intensity in dB HL where the person identified 50% of the speech material correctly. This is the speech recognition threshold or SRT.

Point B on the graph shows the maximum speech recognition score which informs the clinician of the maximum score the subject obtained.

Point C on the graph shows the reference speech recognition curve; this is specific to the test material used (e.g., AB words) and method of presentation (e.g., headphones), and shows a curve which describes the median speech recognition scores at multiple intensities for a group of normal hearing individuals.

Having this displayed on a single graph can provide a quick and easy way to determine and analyze the ability of the person to hear speech and compare their results to a normative group. Lastly, you can use the speech audiogram to identify roll-over. Roll-over occurs when the speech recognition deteriorates at loud intensities and can be a sign of retro-cochlear hearing loss. We will discuss this further in the interpretation section.

Masking in speech recognition testing

Just like in audiometry, cross hearing can also occur in speech audiometry. Therefore, it is important to mask the non-test ear when testing monaurally. Masking is important because word recognition testing is usually performed at supra-threshold levels. Speech encompasses a wide spectrum of frequencies, so the use of narrowband noise as a masking stimulus is not appropriate, and you need to modify the masking noise for speech audiometry. In speech audiometry, speech noise is typically used to mask the non-test ear.

There are several approaches to calculating required masking noise level. An equation by Coles and Priede (1975) suggests one approach which applies to all types of hearing loss (sensorineural, conductive, and mixed):

• Masking level = D S plus max ABG NT minus 40 plus E M

It considers the following factors.

1. Dial setting

D S is the level of dial setting in dB HL for presentation of speech to the test ear.

2. Air-bone gap

Max ABG NT is the maximum air-bone gap between 250 to 4000 Hz in the non‑test ear.

3. Interaural attenuation

Interaural attenuation: The value of 40 comes from the minimum interaural attenuation for masking in audiometry using headphones (for insert earphones, this would be 55 dB).

4. Effective masking

E M is effective masking. Modern audiometers are calibrated in E M , so you don’t need to include this in the calculation. However, if you are using an old audiometer calibrated to an older calibration standard, then you should calculate the E M .

You can calculate it by measuring the difference in the speech dial setting presented to normal listeners at a level that yields a score of 95% in quiet and the noise dial setting presented to the same ear that yields a score less than 10%. 

You can use the results from speech audiometry for many purposes. The below section describes these applications.

1. Cross-check against pure tone audiometry results

The cross-check principle in audiology states that no auditory test result should be accepted and used in the diagnosis of hearing loss until you confirm or cross-check it by one or more independent measures (Hall J. W., 3rd, 2016). Speech-in-quiet testing serves this purpose for the pure tone audiogram.

The following scores and their descriptions identify how well the speech detection threshold and the pure tone average correlate (Table 2).

Table 2: Correlation between speech detection threshold and pure tone average.

If there is a poor correlation between the speech detection threshold and the pure tone average, it warrants further investigation to determine the underlying cause or to identify if there was a technical error in the recordings of one of the tests.

2. Detect asymmetries between ears

Another core use of speech audiometry in quiet is to determine the symmetry between the two ears and whether it is appropriate to fit binaural amplification. Significant differences between ears can occur when there are two different etiologies causing hearing loss.

An example of this could be a patient with sensorineural hearing loss who then also contracts unilateral Meniere’s disease . In this example, it would be important to understand if there are significant differences in the word recognition scores between the two ears. If there are significant differences, then it may not be appropriate for you to fit binaural amplification, where other forms of amplification such as contralateral routing of sound (CROS) devices may be more appropriate.

3. Identify if further testing is required

The results from speech audiometry in quiet can identify whether further testing is required. This could be highlighted in several ways.

One example could be a severe difference in the SRT and the pure tone average. Another example could be significant asymmetries between the two ears. Lastly, very poor speech recognition scores in quiet might also be a red flag for further testing.

In these examples, the clinician might decide to perform a test to detect the presence of cochlear dead regions such as the TEN test or an ACT test to get more information.

4. Detect retro-cochlear hearing loss

In subjects with retro-cochlear causes of hearing loss, speech recognition can begin to deteriorate as sounds are made louder. This is called ‘roll-over’ and is calculated by the following equation:

• Roll-over index = (maximum score minus minimum score) divided by maximum score

If roll-over is detected at a certain value (the value is dependent on the word list chosen for testing but is commonly larger than 0.4), then it is considered to be a sign of retro-cochlear pathology. This could then have an influence on the fitting strategy for patients exhibiting these results.

It is important to note however that as the cross-check principle states, you should interpret any roll-over with caution and you should perform additional tests such as acoustic reflexes , the reflex decay test, or auditory brainstem response measurements to confirm the presence of a retro-cochlear lesion.

5. Predict success with amplification

The maximum speech recognition score is a useful measure which you can use to predict whether a person will benefit from hearing aids. More recent, and advanced tests such as the ACT test combined with the Acceptable Noise Level (ANL) test offer good alternatives to predicting hearing success with amplification.

Just like in pure tone audiometry, the stimuli which are presented during speech audiometry require annual calibration by a specialized technician ster. Checking of the transducers of the audiometer to determine if the speech stimulus contains any distortions or level abnormalities should also be performed daily. This process replicates the daily checks a clinicians would do for pure tone audiometry. If speech is being presented using a sound field setup, then you can use a sound level meter to check if the material is being presented at the correct level.

The next level of calibration depends on how the speech material is delivered to the audiometer. Speech material can be presented in many ways including live voice, CD, or installed WAV files on the audiometer. Speech being presented as live voice cannot be calibrated but instead requires the clinician to use the VU meter on the audiometer (which indicates the level of the signal being presented) to determine if they are speaking at the correct intensity. Speech material on a CD requires daily checks and is also performed using the VU meter on the audiometer. Here, a speech calibration tone track on the CD is used, and the VU meter is adjusted accordingly to the desired level as determined by the manufacturer of the speech material.

The most reliable way to deliver a speech stimulus is through a WAV file. By presenting through a WAV file, you can skip the daily tone-based calibration as this method allows you to calibrate the speech material as part of the annual calibration process. This saves the clinician time and ensures the stimulus is calibrated to the same standard as the pure tones in their audiometer. To calibrate the WAV file stimulus, the speech material is calibrated against a speech calibration tone. This is stored on the audiometer. Typically, a 1000 Hz speech tone is used for the calibration and the calibration process is the same as for a 1000 Hz pure tone calibration.

Lastly, if the speech is being presented through the sound field, a calibration professional should perform an annual sound field speaker calibration using an external free field microphone aimed directly at the speaker from the position of the patient’s head.

Coles, R. R., & Priede, V. M. (1975). Masking of the non-test ear in speech audiometry .  The Journal of laryngology and otology ,  89 (3), 217–226.

Graham, J. Baguley, D. (2009). Ballantyne's Deafness, 7th Edition. Whiley Blackwell.

Hall J. W., 3rd (2016). Crosscheck Principle in Pediatric Audiology Today: A 40-Year Perspective .  Journal of audiology & otology ,  20 (2), 59–67.

Katz, J. (2009). Handbook of Clinical Audiology. Wolters Kluwer.

Killion, M. C., Niquette, P. A., Gudmundsen, G. I., Revit, L. J., & Banerjee, S. (2004).  Development of a quick speech-in-noise test for measuring signal-to-noise ratio loss in normal-hearing and hearing-impaired listeners . The Journal of the Acoustical Society of America , 116 (4), 2395–2405.

Stach, B.A (1998). Clinical Audiology: An Introduction, Cengage Learning.

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What is Speech-In-Noise (SIN)?

Speech-in-Noise (SIN) is undertaken as Speech Audiometry with a background noise present. The most commonly used assessment undertaken in this format is QuickSIN which was developed by Etymotic Research. The QuickSIN is quick and easy to administer. Other assessments can be performed with adult clients and this can be adapted based on their language ability as well as hearing ability.

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Frequently Asked Questions

Why is the sin test important.

Difficulty with hearing in background noise is a common complaint among hearing aid users. Therefore, the measurement of SNR loss (signal-to-noise ratio loss) is important because a person's ability to understand speech in noise cannot be reliably predicted from the pure tone audiogram.

How to Perform SIN?

Performing Speech In Noise tests are done depending on the full instructions of the specific test. All tests should be performed in a soundproof room by a qualified audiology professional.

Who should get a SIN test?

Anyone who experiences difficulty understanding speech in noisy environments should consider taking a SIN test. This includes people with normal hearing sensitivity who still struggle to follow conversations in challenging auditory settings.

How long does the test take?

The duration varies depending on the specific test, but most SIN tests can be completed in 15 to 30 minutes.

Can the SIN test be performed on children?

Yes, there are SIN tests specifically designed for children, although the age at which a child can reliably take the test will depend on their developmental stage.

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Communication Processes

Noise/interference in communication processes.

• Physical noise
• Physiological noise
• Technical noise
• Organizational noise
• Cultural noise
• Psychological noise
• Semantic noise (language, words)

Physical Noise

Physical noise is interference that comes from an external source, or the environment in which the communication is occurring.  Static on a phone call, meeting rooms in a building near an airport’s flight path, conversations during a presentation, not muting your sound while typing during an online meeting all constitute physical noise.  Physical noise also can be non-auditory in nature. Pop-ups create visual noise in an online environment, just as a co-worker gesturing outside of your office window while you are in an online meeting creates visual noise. Sometimes you can control physical noise, as in asking directly at the start of on online meeting for participants to mute their sound when they are not talking.  Other times you will have no control over physical noise.  As a communicator, realize that you’ll need to be prepared to deal with physical noise.

Some strategies to help your audience understand your message, even with physical noise present, include repeating key information, following up an in-person meeting or presentation with an emailed summary, or repeating questions that participants ask during an online meeting.

Physiological Noise

Physiological noise deals with your own abilities to see and hear, your state of health, whether you are tired or hungry at the time of the communication, or any of many different physiological issues that can interfere with paying attention to a message.  While you cannot do much as a communicator to allay other individuals’ physiological noise, you can pick up visual cues during in-person, real-time communications and adjust your message accordingly.  For example, you can speak more slowly or loudly, or be more succinct if you see your audience’s interest waning before lunch.  For both in-person and electronic communications, you can offer electronic versions of your information to audience members who may need to increase font size.  Be aware that physiological noise exists, and be prepared to adjust to the communication situation and your audience’s needs.

Technical Noise

Technical equipment issues can interfere with your audience receiving and understanding your message.  Online or video conferencing equipment may not work for everyone, connectivity may be slow, or servers may go down. To reduce technical noise, make sure that you practice with the equipment you need to use, and have a back-up plan for communicating lengthy or very important messages using a lower-tech format.

Organizational Noise

Organizational noise can occur if you are unaware of, or disregard, expected communication channels in your organization.  Some organizations are structured so that employees at certain levels only communicate with employees at similar levels, while other organizations are less structured with their communication channels.  As a communicator, make sure you understand your organizational culture as much as possible. Don’t be afraid to ask peers or supervisors about appropriate channels of communication so that others focus on your message and not the route or persons to whom it was sent.

Cultural Noise

Cultural noise occurs when cultural expectations, etiquette, attitudes, and values differ.  Many different cultures exist based on nationalities, ages, genders, regions, social positions, work groups, and more, and individuals belong to multiple cultures.  As a communicator, your task is to try to reduce cultural noise by being as informed as possible about your communication audience; trying to anticipate and address questions from other points of view; and using inclusive, non-biased language.

The following video was created by Japanese students to teach the concept of noise.  From your perspective as a student in the U.S., what would create cultural noise for you if you were on assignment in Japan as a new hire in this organization?

After viewing the video, consider what you might do as a communicator to reduce cultural noise for a new hire from Japan who is now working in your organization in the U.S.

Psychological Noise

Psychological noise occurs as a result of personal attitudes, assumptions, and biases.  People have particular perspectives and world views; communication noise occurs when content, language, and perceived attitudes of the communicator and the audience do not mesh. Just as with cultural noise, your task as a communicator dealing with psychological noise is to realize that people will interpret your message differently, depending on their own perspectives.  Try to reduce psychological noise by offering your communication very clearly and directly, using inclusive and unbiased language, and responding calmly and thoughtfully to questions and issues raised.

Semantic Noise

Semantic noise deals with words and language.  Is the language of the communication clear and easy to understand?  Is it free from professional jargon (if the audience is at a low or mixed level of professional understanding)?  Are abstract concepts backed up by concrete examples? Is the language free from grammatical and technical errors?  Are the sentences clear in their structure and easy to read or listen to?  Are concepts offered in an order logical to the communication’s purpose and appropriate to its audience?  Is there too much information, and/or are there too many words?  All of these language issues, however small, can derail focus from the content of your message. As an example, you may have read documents in which the writer consistently uses “its/it’s” incorrectly, or you may have listened to speakers who constantly say “uh” while speaking.  Have you found yourself more focused on counting the “its” or the “uhs” more than listening to the message?

Example of semantic & cultural noise

Cultural expressions and expectations differ not only internationally, but also on many different dimensions from regional to interpersonal.

Someone raised in a rural environment in the Pacific Northwest may have a very different interpretation of “downtown” from someone raised in New York City. To the rural resident, downtown refers to a place, such as the center or urban area of any big city, no matter where that place is located. To a person who lives in or near New York City, though, downtown may be a direction that is more southerly, more than a place. One can go uptown, toward the Metropolitan Museum, or downtown, toward the 9/11 Memorial.  When asked, “Where are you from?,” a New Yorker’s answer may refer to a different sort of place such as a borough (“I grew up in Manhattan”) or a neighborhood (“I’m from the East Village”).

This example involves people with geographical differences, but we can further subdivide between people raised in the same state from two regions, people of the opposite sex, or people from different generations. The combinations and possibilities for semantic and cultural noise, or other types of noise, are endless.

As a communicator, you should work to eliminate semantic noise through careful revision.  Also, whenever possible, request feedback from others to determine whether your audience understands your language in the way you intended.

The following video delves more fully into semantic and psychological noise.

The following video reviews many types of noise that can derail focus from your communication.  However, the video itself contains some noise—see if you can find it, and consider the effect it has on you.

• Noise/Interference in Communication Processes. Authored by : Susan Oaks. Project : Communications for Professionals. License : CC BY-NC: Attribution-NonCommercial
• image of people with question mark bubbles. Authored by : geralt. Provided by : Pixabay. Located at : https://pixabay.com/photos/group-team-balloons-question-mark-464644/ . License : CC0: No Rights Reserved
• video 10 Barriers to Effective Communication . Provided by : Young Entrepreneurs Forum. Located at : https://www.youtube.com/watch?v=slq1nAhZuqE . License : Other . License Terms : YouTube video
• video Noise in Communication. Authored by : John Edmonds. Provided by : Pearcemayfield. Located at : https://www.youtube.com/watch?v=Zy4T5OhaDUk . License : Other . License Terms : YouTube video
• video Communication Barriers in Workplace. Authored by : Alwyn Fong. Located at : https://www.youtube.com/watch?v=9GqWfGav7ic . License : Other . License Terms : YouTube video
• Example of Semantic & Cultural Noise adapted from Business Communication for Success, page 4.6 on Overcoming Barriers to Effective Written Communication. Provided by : University of Minnesota Libraries Publishing. Located at : https://open.lib.umn.edu/businesscommunication/chapter/4-6-overcoming-barriers-to-effective-written-communication/ . Project : Business Communication for Success. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

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Sound masking is the process of adding background sound to reduce noise distractions, protect speech privacy and increase office comfort

Why Do You Need Sound Masking?

Conversations over 15 feet away will fade into the background, making it easier for employees to concentrate.

Employees can speak more freely knowing their conversations won’t be overheard by people across the room or in adjacent private offices.

Improve Workplace Acoustics

Sound masking helps create a balanced, comfortable acoustical environment that’s not too quiet, not too loud, but just right.

How Does Sound Masking Work?

Sound masking is ambient background sound engineered to match the frequency of human speech for greater speech privacy.

Adding sound to a space actually makes the space seem quieter. It sounds counter-intuitive but it’s true. This is because the added sound reduces the intelligibility of human speech. When you can’t understand what someone is saying, their words are less distracting — in fact, you probably don’t even notice them.

Sound masking makes a building seem quieter by raising the ambient noise level of an environment and making speech noise less intelligible and therefore less distracting.

Sound masking is an ambient sound, similar to the sound of airflow, that’s specifically engineered to the frequency of human speech you can target conversational distractions and make them less distracting. Sound masking does not cancel sound or eliminate all speech noise in an environment; it simply reduces how far away conversations can be heard and understood by others, which we call the radius of distraction.

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Radius of distraction is reduced with Sound Masking

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The employees were being distracted by conversations 60 feet away. When the system’s on, speech becomes unintelligible at a distance of about 20 feet.

With CSM’s sound masking in place, there is less distraction from unwanted sounds and conversations. Patients and staff can now experience the positive ambiance we wanted to achieve through the open design concept, and we gained a greater level of patient satisfaction.

Libraries are supposed to be quiet, but whispers could be heard 40 feet away. With the help of Cambridge Sound Management, we now have a library that seems quieter and is still beautiful. We are delighted.

Utilizing sound masking from Cambridge Sound Management keeps the cross talk between the offices and corridors down so you don’t hear inter-office conversations. I highly recommend sound masking as a solution for open-concept office spaces.

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Once you have sound masking installed in your office, you wonder how you ever lived without it.

Sound masking from Cambridge Sound Management allowed us to be true to our architectural vision for our new office without sacrificing acoustical comfort or employee productivity. We love it!

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How is Sound Masking Different Than White Noise?

Sound masking is often referred to as “white noise” but as you can see on the chart on the left, their sound curves vary significantly.

Unlike white noise, sound masking is specifically engineered to match the frequencies of human speech and to sound comfortable, even pleasant, to the human ear. When implemented properly, sound masking should just fade into the background “hum” of a workplace while simultaneously making speech more difficult to hear and understand.

Conversely, the frequency of white noise would be extremely irritating if it were amplified to a volume that would be effective for masking human speech — think “loud AM radio static.” It might cover up the sounds of human speech, but not effectively or efficiently since it is not specifically engineered to do so.

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Speech-In-Noise Test Results for Oticon Opn

Aug 28, 2017 | Evaluation | 0 |

Tech Topic | September 2017  Hearing Review

by Douglas L. Beck, AuD, and Nicolas LeGoff, PhD

A discussion of signal-to-noise ratio problems and solutions, and a summary of findings of a study involving Oticon Opn hearing aids.

The most common problem experienced by people with hearing loss and people wearing traditional hearing aids is not that sound isn’t loud enough. The primary issue is understanding speech-in-noise (SIN). Hearing is the ability to perceive sound, whereas listening is the ability to make sense of, or assign meaning to, sound.

As typical hearing loss (ie, presbycusis, noise-induced hearing loss) progresses, outer hair cell loss increases and higher frequencies become increasingly inaudible. As hearing loss progresses from mild (26 to 40 dB HL) to moderate (41 to 70 dB) and beyond, distortions increase, disrupting spectral, timing, and loudness perceptions. The amount of distortions vary, and listening results are not predictable based on an audiogram, nor are they predictable based on word recognition scores from speech in quiet. 1,2

To provide maximal understanding of speech in difficult listening situations, the goal of hearing aid amplification is twofold: Make speech sounds audible and increase the signal-to-noise ratio (SNR). 1 That is, the goal is to make it easier for the brain to identify, locate, separate, recognize, and interpret speech sounds. “Speech” (in this article) is the spoken signal of primary interest and “noise” is the secondary sound of other people speaking (ie, “speech babble noise”).

The essence of the SIN problem is that the primary speech sounds and secondary sounds (ie, noise) are essentially the same thing! That is, both speech and speech babble noise originate with human voices with similar spectral and loudness attributes, rendering the “SIN” problem difficult to solve.

Signal-to-Noise Ratio (SNR) Loss

Killion 1 reported people with substantial difficulty understanding speech-in-noise may have significant “SNR loss.” Of note, the SNR loss is unrelated to, and cannot be predicted from, the audiogram. Killion defines SNR loss as the increased SNR needed by an individual with difficulty understanding speech in noise, as compared to someone without difficulty understanding speech in noise. He reports people with relatively normal/typical SNR ability may require a 2 dB SNR to understand 50% of the sentences spoken in noise, whereas people with mild-moderate sensorineural hearing loss may require an 8 dB SNR to achieve the same 50% performance. Therefore, for the person who needs an 8 dB SNR, we subtract 2 dB (normal performance) from their 8 dB score, resulting in a 6 dB SNR loss.

Wilson 2 evaluated more than 3,400 veterans. He, too, reported speech in quiet testing does not predict speech-in-noise ability, as the two tests (speech in quiet, speech-in-noise) reflect different domains of auditory function. He suggested the Words-in-Noise (WIN) test should be used as the “stress test” for auditory function.

Beck and Flexer 3 reported, “Listening is where hearing meets the brain.” They said the ability to listen (ie, to make sense of sound) is arguably a more important construct, and reflects a more realistic representation of how people manage in the noisy real world, than does pure-tone hearing thresholds reflected on an audiogram. Indeed, many animals (including dogs and cats) “hear” better than humans. However, humans are atop the food chain not because of their hearing, but due to their ability to listen —to apply meaning to sound. As such, a speech-in-noise test is more of a listening test than a hearing test. Specifically, listening in noise depends on a multitude of cognitive factors beyond loudness and audibility, and includes speed of processing, working memory, attention, and more.

McShefferty et al 4 report SNR plays a vital role for hearing-impaired and normal-hearing listeners. However, the smallest detectable SNR difference for a person with normal hearing is about 3 dB, and they report for more clinically-relevant tasks, a 6 to 8 dB SNR may be required.

In this article, we’ll address concepts and ideas associated with understanding speech- in-noise, and, importantly, we’ll share results obtained while comparing SIN results with Oticon Opn and two major competitors, in a realistic acoustic environment.

Traditional Strategies to Minimize Background Noise

As noted above, the primary problem associated with hearing loss and hearing aid amplification in understanding speech, is noise. The major focus over the last four decades or so has been to reduce background noise. Two processing strategies have been employed in modern hearing aid amplification systems to minimize background noise: digital noise reduction and directional microphones.

Digital Noise Reduction (DNR). Venema 5 reports (p 335) the goal of DNR is noise reduction. DNR systems can recognize and reduce the signature amplitude of steady-state noise using various amplitude modulation (AM) detection systems. AM systems can identify differences in dynamic human speech as opposed to steady state noise sources, such as heating-ventilation/air-conditioning (HVAC) systems, electric motor and fan noise, 60 cycle noise, etc. 6 However, DNR systems are less able to attenuate secondary dynamic human voices in close proximity to the hearing aid, such as nearby loud voices in restaurants, cocktail parties, etc, because the acoustic signature of people we desire to hear and the acoustic signature of other people in close proximity (ie, speech babble noise) are essentially the same.

Venema 5 states (p 331) the broadband spectrum of speech and the broadband spectrum of noise intersect and overlap, and, consequently, are very much the same thing. Nonetheless, given multiple unintelligible speakers and a physical distance of perhaps 7-10 meters from the hearing aid microphone, Venema and Beck 7 report the secondary signal (ie, speech babble noise) may present as (or morph into) more of steady state “noise-like” signal and be attenuated via DNR—providing additional comfort to the listener.

McCreery et al 8 reported their evidence-based systematic review to examine “the efficacy of digital noise reduction and directional microphones.” They searched 26 databases seeking contemporary publications (published after 1980), resulting in four articles on DNR and seven articles on directional microphone studies. Of tremendous importance, McCreery and colleagues? concluded DNR did not improve or degrade speech understanding.

Beck and Behrens 9 reported DNR may offer substantial “cognitive” benefits, including more rapid word learning rates for some children, less listening effort, improved recall of words, enhanced attention, and quicker word identification, as well as better neural coding of words. They suggested typical hearing aid fitting protocols should include activation of the DNR circuit.

Pittman et al 10 reported DNR provides little or no benefit with regard to improved word recognition in noise. Likewise, McCreery and Walker 11 note DNR circuits are routinely recommended for the purpose of improving listening comfort, but restated that, with regard to school-age children with hearing loss, DNR neither improved nor degraded speech understanding.

Directional Microphones (DMs). DMs (or “D-mics”) are the only technology proven to improve SNR. However, the likely perceived benefit from DMs in the real world, due to the prevalence of open canal fittings, is often only 1-2 dB. 7 Directivity Indexes (DIs) indicating 4-6 dB improvement are generally not “real world” measures. That is, DIs are generally measured on manikins, in an anechoic chamber, based on pure tones, and DIs quantify and compare sounds coming from the front versus all other directions.

Nonetheless, although an SNR improvement of 1-2 dB may appear small, every 1 dB SNR improvement may provide a 10% word recognition score increase. 5,12,13

In their extensive review of the published literature, McCreery et al 8 reported that, in controlled optimal situations, DMs did improve speech recognition; yet they cautioned the effectiveness of DMs in real-world situations was not yet well-documented and additional research is needed prior to making conclusive statements.

Brimijoin and colleauges 14 stated directionality potentially makes it difficult to orient effectively in complex acoustic environments. Picou et al 15 reported directional processing did reduce interaural loudness differences (ILDs), and localization was disrupted in extreme situations without visual cues. Research by Best and colleagues 16 found narrow directionality is only viable when the acoustic environment is highly predictable. Mejia et al 17 indicated, as beam-width narrows, the possible SNR enhancement increases; however, as the beam-width narrows, the possibility also increases that “listeners will misalign their heads, thus decreasing sensitivity to the target…” Geetha et al 18 reported “directionality in binaural hearing aids without wireless communication” may disrupt interaural timing and interaural loudness cues, leading to “poor performance in localization as well as in speech perception in noise…”

New Strategies to Minimize Background Noise

Research by Shinn-Cunningham and Best 19 suggests the ability to selectively attend depends on the ability to analyze the acoustic scene and form perceptual auditory objects properly. If analyzed correctly, attending to a particular sound source (ie, voice) while simultaneously suppressing background sounds may become easier as one successfully increases focus and attention.

Therefore, the purpose of a new strategy for minimizing background noise should be to facilitate the ability to attend to one primary sound source and switch attention when desired—which is what Oticon’s recently- released Multiple Speaker Access Technology (MSAT) has been designed to do.

Multiple Speaker Access Technology (MSAT). In 2016, Oticon introduced MSAT. The goals of MSAT are to selectively reduce disturbing noise while maintaining access to all distinct speech sounds and to support the ability of the user to select the voice they choose to attend to.

MSAT represents a new class of speech enhancement technology and is intended to replace current directional and noise reduction systems. MSAT does not isolate one talker; it maintains access to all distinct speakers. MSAT is built on three stages of sound processing:

1) Analyze provides two views of the acoustic environment. One view is from a 360 degree omni microphone; the other is a rear-facing cardioid microphone to identify which sounds originate from the sides and rear. The cardioid mic provides multiple noise estimates to provide a spatial weighting of noise.

2) Balance increases the SNR by constantly acquiring and mixing the two mics (similar to auditory brainstem response or radar) to obtain a rebalanced soundscape in which the loudest noise sources are attenuated. In general, the most important sounds are present in the omni view, while the most disturbing sounds are present in both omni and cardioid views. In essence, cardioid is subtracted from omni, to effectively create nulls in the direction of the noise sources, thus increasing the prominence of the primary speaker.

3) Noise Removal (NR) provides very fast removal of noise between words and up to 9 dB of noise attenuation. Importantly, if speech is detected in any band, Balance and NR systems are “frozen” so as to not isolate the front talker, but to preserve all talkers.

The Oticon Opn system uses MSAT and, therefore, it is neither directional nor omnidirectional. To be clear, the goal of DMs is to pick up more sound from the front of the listener, as compared to sounds from other angles, and D-mics do not help one tell the direction of sounds, nor do they increase the intensity of sounds coming from the front—or as Venema states (p 316), “they simply decrease the intensity of sounds coming from the sides and rear, or relative to sounds coming from the front…” 5 According to Geetha et al, 18 DMs are designed to provide attenuation of sounds emerging from the sides of the listener, and Venema 5 states omnidirectional microphones are “equally sensitive to sounds coming from all directions…” Thus, omnidirectional microphones theoretically have a DI of 0.

Therefore, MSAT is neither an omni or a directional system, but does represent a new technology.

Spatial Cues. Of significant importance to understanding SIN is the ability to know where to focus one’s attention. Knowing “where to listen” is important with regard to increasing focus and attention. Spatial cues allow the listener to know where to focus attention and, consequently, what to ignore or dismiss. 20 The specific spatial cues required to improve the ability to understand SIN are interaural level differences (ILDs) and interaural time differences (ITDs), such that the left and right ears receive unique spatial cues.

Sockalingham and Holmberg 21 presented laboratory and real-world results demonstrating “strong user preference and statistically significant improved ratings of listening effort and statistically significant improvements in real-world performance resulting from Spatial Noise Management.”

Beck 22 reported spatial hearing allows us to identify the origin/location of sound in space and attend to a “primary sound source in difficult listening situations” while ignoring secondary sounds. Knowing “where to listen” allows the brain to maximally listen in difficult/noisy listening situations, as the brain is better able to compare and contrast unique sounds from the left and right ears—in real time—to better determine where to focus attention.

Geetha et al 18 stated speech in noise is challenging for people with sensorineural hearing loss (SNHL). To better understand speech in noise, the responsible acoustic cues are ITDs and ILDs. They report “…preservation of binaural cues is…crucial for localization as well as speech understanding…” and directionality in binaural amplification without synchronized wireless communication can disrupt ITDs and ILDs, leading to “…poor performance in localization…(and) speech perception in noise…” 18

Oticon Opn uses Speech Guard™ LX to improve speech understanding in noise by preserving clear sound quality and speech details, such as spatial cues via ILDs and ITDs. It uses adaptive compression and combines linear amplification with fast compression to provide up to a 12 dB dynamic range window, preserving natural amplitude cues in speech signals. Likewise, Spatial Sound™ LX helps the user locate, follow, and shift focus to the primary speaker via advanced technologies to provide a more precise spatial awareness for identify where sounds originate.

The Oticon OPN SNR Study

In Pittman et al’s research, 10 they stated, “Like the increasingly unique advances in hearing aid technology, equally unique approaches may be necessary to evaluate these new features…” The purpose of this study was to compare the results obtained using Oticon Opn to two other major manufacturers with regard to listeners’ ability to understand speech in noise in a lab-based, yet realistic, background noise situation.

Admittedly, despite the fact that no lab-based protocol perfectly replicates the real world, this study endeavored to realistically simulate what a listener experiences as three people speak sequentially from three locations, without prior knowledge as to which person would speak next.

Methods. A total of 25 German native-speaking participants (ie, listeners) with an average age of 73 years (SD: 6.2 years) with mild-to-moderate symmetric SNHL underwent listening tasks. Each participant wore the Oticon Opn 1 miniRITE with Open Sound Navigator set to the strongest noise reduction setting. Power domes were worn with each of the three hearing aids.

The results obtained with Opn were compared to the results from two other major manufacturers’ (Brand 1 and 2) solutions, using directionality and narrow directionality/beamforming (respectively). All hearing aids were fitted using the manufacturer’s fitting software and earmold recommendations, based on the hearing loss and other gathered data. The level of amplification for each hearing aid was provided according to NAL-NL2 rationale.

The primary measure reported here was the Speech Reception Threshold-50 (SRT-50). The SRT- 50 is a measure that reflects the SIN level at which the listener correctly identifies 50% of the sentence-based keywords correctly. For example, an SRT-50 of 5 dB indicates the listener correctly repeats 50% of the words when the SNR is 5 dB. Likewise, if the SRT is 12 dB, this indicates the listener requires an SNR of 12 dB to achieve 50% correct.

The goal of this study was to measure the listening benefit provided to the listeners in a real-life noisy acoustic situation in which the location of the sound source (ie, the person talking) could not be predicted. That is, three human talkers and one human listener were engaged in each segment of the study (Figure 1). There were 25 separate listeners.

Figure 1. Schematic representation of the acoustical conditions of the experiment.

Speech babble (ISTS) and background noise (speech-shaped) were delivered at 75 dB SPL. The German-language Oldenburg sentence test (OLSA) 23 was delivered to each participant, while wearing each of the three hearing aids.

The OLSA Matrix Tests are commercially available and are accessible via software-based audiometers. We conducted these tests with an adaptive procedure targeting the 50% threshold of speech intelligibility in noise (the speech reception threshold, or SRT). As noted above, speech noise was held constant at 75 dB SPL while the OLSA speech stimuli loudness varied to determine the 50% SRT using a standard adaptive protocol. Each of the 25 listeners was seated centrally and was permitted to turn his/her head as desired to maximize their auditory and visual cues. The background speech noise was a mix of speech babble delivered continuously to the sound-field speakers at ±30° (relative to the listener) and simultaneously at 180° behind the listener, as illustrated in Figure 1. Each of the 25 listeners was seated centrally while three talkers were located in front of, as well as ±60° (left and right) of the listener. Target speech was randomly presented from one of the three talker locations. Listeners were free to turn their heads as desired.

Results. While listening to conversational speech, the average scores obtained from all three talkers by the 25 listeners using directionality (Brand 1) demonstrated an average SRT of -4.9 dB. Listeners using narrow directionality/beamforming (Brand 2) demonstrated an average SRT of -5.5 dB. Listeners wearing Oticon OPN had an average SRT of -6.3 dB (see Figures 2-4).

Figure 2. Overall results. Average improvement in SNR and anticipated improvement in word recognition. Bar heights represent the average SRT-50. Each improvement bar is statistically different from the others at p<0.05.

Figure 3. Average speech understanding of center speaker. The average SRT-50 for the hearing aid with directionality was significantly lower than the average SRT obtained with the two other hearing aids. The average SRT-50 obtained with the narrow directionality and OpenSound Navigator were not significantly different from each other, although narrow directionality and Open Sound Navigator were each statistically and significantly better than directionality. The right-facing arrow indicates an approximate 20% word recognition improvement using either narrow directionality or Open Sound Navigator, as compared to directionality.

Figure 4. Left and right speaker results. The average SRT-50s obtained from speakers located at ±60° using directionality and narrow directionality were not significantly different from each other. The average SRT-50 obtained with Open Sound Navigator was statistically and significantly better than those obtained with other technologies. The right-facing arrow indicates 15% likely improvement in word recognition with Open Sound Navigator as compared to directionality or narrow directionality.

Of note, it is generally accepted that for, each decibel of SNR improvement, the listener likely gains some 10% with regard to word recognition scores.

Realistic listening situations are difficult to replicate in a lab-based setting. Nonetheless, the lab-based setup described here is relatively new and is believed to better replicate real-life listening situations than is the typical “speech in front” and “noise in back” scenario.

Obviously, important speech sounds occur all around the listener while ambulating through work, recreational, and social situations. The goal of the amplification system, particularly in difficult listening situations, is to make speech sounds more audible while increasing the SNR to make it easier for the brain to identify, locate, separate, recognize, and interpret speech sounds.

Oticon’s Open Sound Navigator (OSN) with Multi Speaker Access Technology (MSAT) has been shown to allow (on average) improved SNR-50s and improved word recognition scores in noisy situations, such as when speech and noise surrounds the listener. OSN with MSAT allows essentially the same word recognition scores as the best narrow directional protocols when speech originates in front of the listener, while dramatically improving access to speakers on the sides and, therefore, delivering an improved sound experience. OSN with MSAT effectively demonstrates improved word recognition scores in noise when speech originates around the listener .

The results of this study demonstrate that Oticon’s Open Sound Navigator provides overall improved overall word recognition in noise when compared to directional and narrow directionality/beamforming systems. We hypothesize these results are due to Multiple Speaker Access Technology, as well as the maintenance of spatial cues and many other advanced features available in Oticon Opn.

Correspondence  can be addressed to HR or Dr Beck at: [email protected]

Citation for this article:  Beck DL, LeGoff N. Speech-in-noise test results for Oticon Opn.  Hearing Review . 2017;24(9):26-30.

 References

Killion MC. New Thinking on Hearing in Noise—A Generalized Articulation Index. Seminars Hearing. 2002;23(1):57-75.

Wilson RH. Clinical experience with the words-in-noise test on 3430 veterans: comparisons with pure-tone thresholds and word recognition in quiet. J Am Acad Audiol. 2011;22(7)[Jul-Aug]:405-423.

Beck DL, Flexer C. Listening is where hearing meets brain…in children and adults. Hearing Review. 2011;18(2):30-35.

McShefferty D, Whitmer WM, Akeroyd MA. The just noticeable difference in speech t0 noise ratio. Trends in Hearing . February 12, 2015;19.

Venema TH. Compression for Clinicians. A Compass for Hearing Aid Fittings. 3rd ed. San Diego: Plural Publishing;2017.

Mueller HG, Ricketts TA, Bentler R. Speech Mapping and Probe Microphone Measurements. San Diego: Plural Publishing;2017.

Beck DL, Venema TH. Noise reduction, compression for clinicians, and more: An interview with Ted Venema, PhD. Online July 27, 2017. Available at:  https://hearingreview.com/2017/07/noise-reduction-compression-clinicians-interview-ted-venema-phd/

McCreery RW, Venediktov RA, Coleman JJ, Leech HM. An evidence-based systematic review of directional microphones and digital noise reduction hearing aids in school-age children with hearing loss. Am J Audiol. 2012; 21(2)[Dec]:295-312.

Beck DL, Behrens T. The Surprising Success of Digital Noise Reduction. Hearing Review. 2016;23(5):20-22.

Pittman AL, Stewart EC, Willman AP, Odgear IS. Word recognition and learning–Effects of hearing loss and amplification feature. Trends Hearing . 2017; 21:1-13.

McCreery RW, Walker EA. Hearing aid candidacy and feature selection for children. In: Pediatric Amplification–Enhancing Auditory Access. San Diego: Plural Publishing;2017.

Chasin M. Slope of PI function is not 10%-per-dB in noise for all noises and for all patients. Hearing Review. 2013;22(10):12.

Taylor B, Mueller HG. Fitting and Dispensing Hearing Aids. 2nd ed. San Diego: Plural Publishing;2017.

Brimijoin WO, Whitmer WM, McShefferty D, Akeroyd MA. The effect of hearing aid microphone mode on performance in an auditory orienting task. Ear Hear. 2014;35(5)[Sep-Oct]:e204-212.

Picou EM, Aspell E, Ricketts TA. Potential benefits and limitations of three types of directional processing in hearing aids . Ear Hear. 2014;35(3)[May-Jun]:339-352.

Best V, Mejia J, Freeston K, van Hoesel RJ, Dillon H. An evaluation of the performance of two binaural beamformers in complex and dynamic multitalker environments. Int J Audiol. 2015;54(10):727-735.

Mejia J, Dillon H, van Hoesel R, et al. Loss of speech perception in noise–Causes and compensation. Proceedings of ISAAR 2015: Individual Hearing Loss–Characterization, Modelling, Compensation Strategies . 5th Symposium on Auditory and Audiological Research. Danavox Jubilee Foundation. August 2015:209.

Geetha C, Tanniru K, Rajan RR. Efficacy of directional microphones in hearing aids equipped with wireless synchronization technology. J Int Advanced Otol. 2017; 13(1):113-117.

Shinn-Cunningham B, Best V. Selective Attention in normal and impaired hearing. Trends in Hearing. 2008;12(4):283-299.

Beck DL, Sockalingam R. Facilitating spatial hearing through advanced hearing aid technology. Hearing Review. 2010;17(4):44-47.

Sockalingam R, Holmberg M. Evidence of the effectiveness of a spatial noise management system. Hearing Review. 2010; 17(9):44-47.

Beck DL. BrainHearing: Maximizing hearing and listening. Hearing Review. 2015;21(3):20-23. Available at: https://hearingreview.com/2015/02/brainhearing-maximizing-hearing-listening

Hörtech. Oldenburg Sentence Test (OLSA). Oldenburg, Germany: Hörtech. Available at: http://www.hoertech.de/en/medical-devices/olsa.html

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Training Programs for Improving Speech Perception in Noise: A Review

Nasrin gohari.

1 Hearing Disorders Research Center, Department of Audiology, School of Rehabilitation, Hamadan University of Medical Sciences, Hamadan, Iran

Zahra Hosseini Dastgerdi

2 Department of Audiology, School of Rehabilitation, Isfahan University of Medical Sciences, Isfahan, Iran

Nematollah Rouhbakhsh

3 Department of Audiology, School of Rehabilitation, Tehran University of Medical Sciences, Tehran, Iran

Sara Afshar

Razieh mobini.

Understanding speech in the presence of noise is difficult and challenging, even for people with normal hearing. Accurate pitch perception, coding and decoding of temporal and intensity cues, and cognitive factors are involved in speech perception in noise (SPIN); disruption in any of these can be a barrier to SPIN. Because the physiological representations of sounds can be corrected by exercises, training methods for any impairment can be used to improve speech perception. This study describes the various types of bottom-up training methods: pitch training based on fundamental frequency (F0) and harmonics; spatial, temporal, and phoneme training; and top-down training methods, such as cognitive training of functional memory. This study also discusses music training that affects both bottom-up and top-down components and speech training in noise. Given the effectiveness of all these training methods, we recommend identifying the defects underlying SPIN disorders and selecting the best training approach.

Introduction

Accurate speech perception in everyday life is dependent on the hearing system’s ability to process complex sounds in the presence of background noise. Speech perception in noise (SPIN) is also difficult for children and even some young adults with normal hearing and cognitive abilities. To understand speech in noisy situation, children with normal hearing require a higher signal-to-noise ratio (SNR) than adults. Because the most childhood learning takes place in noisy environments, the emergence of any speech perception disorder in children can result in learning, academic, and communication problems [ 1 ]. Adults with difficulty speech understanding in noise, on the other hand, complain of being tired of listening to or hearing something without understanding the meaning, discomfort from background noise, and misunderstanding of conversations in the presence of competing sounds [ 2 - 4 ]. Speech impairment in noisy environments is one of the most important challenges for children with central auditory processing disorder (CAPD) [ 5 ], learning disabilities (LD) [ 6 ] attention deficit hyperactivity disorder (ADHD) [ 7 ], and hearing loss [ 8 ] and likewise for the elderly (over 65 years) [ 9 - 12 ]. The neurological and processing mechanisms associated with SPIN include pitch perception, neural coding and decoding of temporal and intensity cues, and cognitive skills. Each of these mechanisms is important for SPIN [ 13 ].

Pitch perception is an important indicator in the processing of complex stimuli and SPIN. Speech perception is related to the speaker’s fundamental frequency (F0). The frequency of speech components is used to identify the speaker [ 14 ]. Studies have shown that listeners recognize F0 in the presence of ambient background noise and use other cues such as harmonics and formants [ 15 ]. Oxenham [ 16 ] showed that the ability to receive F0 and pitch is involved in concurrently grouping and segregating speech sounds in normal hearing and people who have hearing loss or those using cochlear implants.

Temporal and intensity cues are related to interaural temporal difference (ITD) and interaural level difference (ILD), respectively. This is the primary condition for distinguishing target auditory data from non-target auditory data and also for normal auditory function [ 17 , 18 ]. Studies have shown that correct localization can help people with normal hearing thresholds to understand conversations with a lower SNR, and also demonstrated that auditory localization improves the SNR by 2 to 3 dB when the nature of the noise and signal are different, but increases the spatial advantage by 10 dB when the noise and signal are homogeneous [ 19 , 20 ]. When the noise and the signal have different frequency textures, the noise causes energetic masking. By contrast, when the noise and the signal of the same type coexist, e.g., target speech in the presence of distorted speech signals, the distracted noise causes both energetic and informational masking. Target signal processing in the presence of masking noises, particularly informational masking, necessitates the proper operation of cognitive mechanisms [ 21 ].

The main cognitive functions are attention, short-term memory, and working memory [ 3 ]. In cases such as hearing loss and the lack of temporal spectral encoding or sensory input, cognitive functions serve as compensatory mechanisms for the auditory system [ 22 ]. Speech perception exhibits a relationship with attention and auditory memory in the presence of noise [ 23 , 24 ]. Studies on patients with auditory attention and memory deficits showed that they had difficulty understanding speech in noise [ 25 , 26 ]. It can be hence concluded that higher levels of cognitive performance through top-down pathways can enhance bottom-up pathways and increase signal quality [ 3 , 27 ]. In addition, some exercises can influence both top-down and bottom-up activities [ 8 ].

The modification of the hearing system using educational tasks to strengthen speech comprehension is one of the most important and common areas of research today. Ample evidence suggests that hearing training exercises can help normal hearing and peoples with hearing loss, to improve spectral and temporal properties perception. Auditory training tasks are divided into three main categories: bottom-up, top-down, and mixed exercises (a combination of bottom-up and top-down methods). They aim to ameliorate auditory events comprehension via repetitive listening tasks [ 28 ]. Bottom-up tasks focus on the acoustic cues of the signals (i.e., spectral, temporal, and intensity characteristics), and require acoustic identification and differentiation. On the other side, top-down tasks improves signal perception by increasing attention to stimuli and encouraging the use of background information [ 28 ].

Various exercises and methods (bottom-up and top-down) have been designed to improve SPIN in different clinical populations, including patients with auditory processing disorder (APD) [ 29 ], the elderly [ 30 - 32 ], hearing-impaired people [ 33 ], and autism patients [ 34 ]. This review study aims to search and analyze all the studies that investigated bottom-up and top-down hearing training programs for improving SPIN in different populations.

Training Programs Based on Cues and Factors Involved in Bottom-up Processes for Improving SPIN

Pitch training.

Equivalent to the human sensory perception of sound frequency, the pitch is one of the psychological components of sound, along with loudness and timber, that aid in understanding music, speech perception, and sound separation in the presence of competing sound sources. Understanding the pitch of compound sounds depends on the discovery of the F0 and its harmonics and the periodicity of sounds [ 35 ]. Harmonics, along with other sound properties (spectral properties, fundamental frequency, synchronicity, or asynchronous at stimulus onset and offset), contribute to the formation of a hearing object by grouping/separating consecutive and concurrent sounds [ 36 ]. Because pitch is an important component of SPIN, various studies have investigated the training programs for improving pitch perception, including fundamental frequency [ 37 , 38 ] and harmonics trainings [ 8 ].

Fundamental frequency (F0) training

Changing the F0 is one of the methods examined in various studies on speech materials to improve pitch differentiation. Vowel differentiation has been used in F0 training studies. Vowels are components of verbal signals that contain a lot of information about F0, low-frequency components (first formant), and second formant. The acoustic information obtained from these formant features carries phonological information and, along with consonants, is considered a basis for speech comprehension [ 39 ]. The effect of F0 differences on the separation of concurrent sound sources is studied using concurrent vowel pairs [ 37 , 40 ]. These results show that increasing F0 differentiation between vowel and target speech from competitive speech improves speech perception [ 38 ].

Vowel interventions aim to improve the ability to distinguish between vowels. Meaningless syllables are frequently used in this training method to reduce the effects of top-down mechanisms (based on the schema and prior knowledge) and enhance vowel comprehension, as the basis of speech transmission and perception, by nourishing bottom-up pathways more accurately. Vowels in various formats are presented during vowel training [ 41 ]. A review of the literature revealed that two studies have specifically investigated the vowel training interventions on hearing-impaired children [ 34 ] and the elderly with normal hearing [ 42 ]. A training pattern proposed by Talebi, et al. [ 33 ] involved teaching five vowel sounds (/æ/, /e/, /ɒː/, /i:/, and /u:/) in a meaningless pattern of monosyllabic vowels including unvoiced syllables as /pæ/, /shæ/, /sæ/, /hæ/, and /kæ/. This training method is repeated for other sounds. The syllables were spoken behind the hearing impaired children, and they were asked to identify and produce syllables verbally. At the end of the training, the effect of this type of training on double-vowel separation was evaluated both behaviorally and electrophysiologically, and the results showed the effects of vowel training on concurrent sound separation [ 32 , 33 ]. There was a difference between pairs of vowels in their fundamental frequency [ 43 ]. Heidari, et al. [ 42 ] conducted a vowel training program based on hearing exercises for the elderly. The training program involved teaching six vowels (/æ/, /e/, /a/, /i/, /o/ and /u/) as meaningless monosyllabic vowels for a period of 5 weeks. For example, the monosyllabic vowels /pæ/, /ʃæ/, /sæ/, /hæ/, and /kæ/ were spoken at a distance of one meter behind the person at a hearing comfortable level. This process was repeated for the vowels /e/, /a/, /i/, /o/ and /u/. This training program also improved SPIN among the elderly.

Harmonic training

Mistuning harmonics is a behavioral [ 8 ] and electrophysiological [ 44 ] method of evaluating concurrent sound separation. It is assumed that when one of the harmonic components is mistuned by 3% of its harmonic value, it is heard as a separate tone from the complex tone. Mistuned harmonics produce and perceive beats as a result of amplitude modulation in the temporal envelope of the stimulus waveform. Increasing the mistuning rate from 3% to 16% and the duration to more than 50 milliseconds results in a decrease in the detection threshold of beats [ 36 ]. Moossavi, et al. [ 8 ] tested the ability of several hearing-impaired children to perceive and distinguish harmonics and, for the first time, designed a new method of harmonic differentiation training. According to the prevalence of the fundamental frequency of human speech sounds in men (100-146 Hz) and women (188-221 Hz) [ 45 ], they employed a complex tone of 100, 200, and 300 Hz with their first 10 harmonics and mistuned one of the first harmonics each time by 2%, 4%, 8%, and 16%. Then they asked the children to compare the complex tone with the tuned harmonic and the mistuned harmonic. The training was performed at frequencies of 100, 200, and 300 Hz in the first, third, fifth, seventh, and ninth harmonics after obtaining the mistuning differentiation threshold in each harmonic of the complex tone. The training began with the lowest level of detection in each harmonic and increased by 70% of the difficulty level if differentiated. The study findings revealed that this training improved the performance of hearing-impaired children in the frequency pattern sequence test and consonant-vowel perception in noise [ 8 ].

Localization training

For sound localization, the human auditory system employs two delicate cues. Horizontal level localization is based on ITD for sounds below 1,500 Hz, and ILD and spectral cues for sounds above 2,500 Hz. These spatial and spectral cues may be important for spatial stream segregation, which aids in speech perception [ 46 ]. ITD is more important to sound localization [ 47 ]. The ITD in unmodulated signals is only processed up to 1,500 Hz and is referred to as the ITD fine structure (ITD FS) [ 48 , 49 ]. A slow carrier (low frequency) modulates the ITD information at higher frequencies, and this is known as ITD envelope (ITD ENV) [ 50 ]. Speech, as a modulated signal, contains two types of ITD: ITD FS and ITD ENV [ 51 ].

Wright and Fitzgerald [ 52 ] evaluated the effectiveness of training based on the simulation of ITD and ILD cues under headphones and reported the improved ability of participants to diagnose ITD and ILD after training. Furthermore, a study by Kuk, et al. [ 53 ] demonstrated the reliability of the localization training program in people with hearing loss. Sound Storm (previously known as the Listen & Learn Auditory Training Software) is a training program designed and evaluated by Cameron, et al. [ 54 ] to improve the binaural processing skills of children with spatial processing disorder (SPD). SPD is defined as the inability of individuals to use binaural cues to selectively pay attention to sounds coming from one direction while suppressing sounds coming from other directions. As a result, children with SPD struggle to understand speech in noisy environments, such as classrooms. The Sound Storm software creates a three-dimensional hearing environment through headphones. The child is required to repeat a word, including the target, which appears in the background noise. A comparative method according to the patient’s response determines the level of sentence intensity. In their study, they used the Sound Storm auditory training software to train 9 suspected SPD children who performed abnormally in the speech-innoise assessment (LiSN-S PGA) for 15 minutes per day for a period of 12 weeks. They examined the effect of the intervention after three months and found that the speech perception threshold improved by 10 dB in the Sound Storm auditory training software [ 54 ].

Lotfi, et al. [ 29 ] studied the effectiveness of an auditory localization rehabilitation program based on ITD cues on spatialized speech in noise and monaural low redundancy tests in a group of children with suspected APD. After 12 training sessions, the mean speech perception score was significantly better in the experimental group in comparison to the control group. Lotfi, et al. [ 30 ] also investigated the effect of a spatial processing training program called the Persian spatialized speech in noise test on speech perception skills in the elderly with normal hearing who complaint of difficulty with SPIN. Their findings revealed that the SNR significantly reduced to 50% in speech perception after the training, whereas auditory spatial rehabilitation could assist the older adults in benefiting from the spatial difference of sound sources and noise for better speech perception.

Recent investigations have dealt with the role of ITD ENV in spatial hearing and SPIN [ 55 , 56 ]. Majdek, et al. [ 51 ] confirmed the role of ITD ENV in speech localization and understanding in noisy environments. In a study by Delphi, et al. [ 31 ] stimulus ENV in various ITDs were presented to the elderly with normal hearing. The ENV was designed in 10-millisecond steps from 10 to 100 milliseconds and 50-millisecond steps from 100 to 350 milliseconds. ITD ENV was initially implemented according to the results of previous assessments and then it was gradually reduced. Each ITD ENV stimulus was presented again and again until the participant correctly identified it. SPIN was re-evaluated at the end of the training to measure the effectiveness of the training. The results revealed that the ITD ENV training not only improved the localization abilities of participants but also increased their mean score of SPIN.

Temporal training

The temporal processing is a broad category of auditory processing abilities that includes temporal sequencing, temporal integration, temporal resolution, and temporal masking [ 32 ]. According to perceptual and neurophysiological studies, temporal processing skills are more affected by age than other central auditory processing skills. There are numerous biological reasons for the auditory system’s poor functioning in the elderly, including decreased myelin integrity, nerve longterm recovery, decreased brain connections, and decreased neural synchronicity [ 57 ]. Inability to process time can be a major cause of language disorders. For example, autism has been linked to temporal processing disorders and their relationship with SPIN [ 34 ]. Because of the importance of time processing, it is necessary to develop training approaches based on this skill. Temporal training programs in the form of formal and organized homework, and even computer games, have been used in studies with various goals [ 58 ]. One of the most important goals of temporal processing training is to improve time and language processing skills, as well as speech perception in noisy environments.

Ramezani, et al. [ 34 ] assessed the effectiveness of temporal processing based rehabilitation on sequencing skills and temporal resolution using temporal pattern detection and noise detection exercises on the speech perception in noise ability and on the speech ABR components in 28 autistic adolescents. Their findings showed a significant improvement in SPIN and the efficiency of time processing at the auditory brainstem level (reduction of wave latency) in response to speech signals. Sattari, et al. [ 32 ] also designed the rehabilitation tasks related to auditory temporal processing in 5 domains, including detection of the number of stimuli, pitch detection, detection of duration patterns, detection of the number of meaningless speech stimuli in noise, and gap detection in noise, to improve SPIN in the elderly aged 75-60 years who were using hearing aids. In another study, Rasouli Fard, et al. [ 59 ] separated the fine temporal structure and the stimuli envelope of vowel-consonantvowel (VCV) and then designed a training method based on the fine temporal structure and differentiation of 16 consonants. Their results showed an improvement in SPIN among the elderly with mild to moderate hearing loss.

Phonemic training

Bottom-up processes (e.g., as sensory speech processing) and top-down processes (e.g., selective attention, short- and long-term memory, and the use of lexical and textual concepts) are always involved in the process of speech perception. Both types of processes are constantly interacting with one another and are inseparable. By reducing semantic information (content and context), individuals rely on phonetic features (such as formant frequencies and voice onset time) for speech perception [ 60 ]. However, it has been reported that bottom-up training protocols improve everyday listening ability less than topdown training [ 61 ]. Schumann, et al. [ 60 ] reported that a computer-based listening training program, which included phonological differentiation using VCV and consonant-vowelconsonant (CVC), was effective in improving SPIN in individuals using cochlear implants. As the most common disorder in auditory processing, phonological differentiation training is one of the components of defect decoding training in the Buffalo model of therapy for APDs [ 62 ]. This training program, which is known as phonemic training program, aims to improve speech comprehension, reading ability, auditory spelling improvement, and speech clarity. According to the results of phonemic error analysis, this training program begins with easier tasks, such as providing phonemes that are easier to perceive for participants, and gradually increases in difficulty as auditory processing goes on. Practices in this training program are based on the repetition and differentiation of learned phonemes [ 39 ].

Training Methods for Improving SPIN Based on Factors Involved in Top-Down Processes

Memory-based training.

Many studies have extensively dealt with cognitive training in recent years and demonstrated its remarkable effects on improving academic performance [ 63 ], and language learning [ 64 ], and lowering the risk of dementia [ 65 ]. Cognitive training includes attention, functional memory, and short-term memory reinforcement [ 3 ]. However, more emphasis is usually placed on improving functional memory [ 63 , 66 ]. As part of the cognitive process, memory related to the reception, process and store of verbal stimuli, and eventually recall what have been heard. Auditory memory serves as the foundation for the development of language skills (including the ability to learn and memorize words as well as the ability to understand and apply grammar, spoken language, and written language) and the learning process. As a result, language could not be imagine without memory [ 67 ]. More extensive functional memory capacities are associated with improved academic performance and language learning as well as a lower risk of pathological aging [ 63 , 66 ]. After functional memory training, the elderly with unknown cognitive impairment showed an increase in functional memory capacity [ 68 , 69 ]. Functional memory has also been shown to play a role in listeners’ ability to understand speech in noisy environments. Among people with normal hearing and those with hearing impairments, listeners with greater functional memory capacity appear to be more successful in understanding speech in noise. Greater functional memory capacity predicts greater success in speech recognition in noise in older people who use hearing aids [ 70 , 71 ]. Because functional memory capacity predicts the success of SPIN, it can be suggested that cognitive training and increasing listeners’ functional memory capacity can be a basis for developing an effective intervention. Many functional auditory memory training tasks rely on straightforward test items including numbers, letters, or monosyllabic words. In addition, since these assignments do not have a high semantic load, they are relatively strong interventions for improving hearing loss [ 72 ]. Ingvalson, et al. [ 73 ] reported that 10 days of reverse digit training significantly improved reading comprehension and SPIN among native Mandarin Chinese speakers and native Chinese English speakers. Therefore, this type of training can be used to improve SPIN.

Music training

Music is a source of pleasure, learning, and well-being. Prolonged exposure to music creates plasticity from the cochlea to the auditory cortex [ 74 ]. Several studies have shown that exposure to music can lead to the transfer of specific listening proficiencies to non-musical domains [ 74 , 75 ]. The theories presented by Patel shows that music education increases adaptive flexibility in speech processing neural networks [ 76 ]. It also reinforces numerous auditory processing components involved SPIN among musicians, including syllable differentiation [ 77 , 78 ], speech temporal signals processing [ 78 ], prosody [ 79 ], pitch and harmonics [ 80 ], melodic contour [ 81 ], and auditory cognition such as working memory [ 82 , 83 ], attention [ 84 , 85 ], and neural representation of speech [ 86 , 87 ].

Zendel and Alain [ 88 ] employed ERP responses to investigate the separation of concurrent sounds in musicians with a mean age of 28 years. N1 and P2 waves were used as evidence to study the music training effectiveness on the separation process. The findings demonstrated the musicians’ exceptional ability to separate concurrent sounds. The researchers in this study hypothesized that training methods, such as music therapy, could have a significant effect on the separation of sounds presented concurrently, ultimately improving sound perception (especially speech). In another study conducted by Swaminathan, et al. [ 89 ], the spatial release of noise was higher among musicians than non-musicians. Parbery-Clark, et al. [ 90 ] demonstrated improvement in speech perception in both multi-talker and continuous pseudo-speech noise in musicians.

Considering the benefits of music in improving the performance of bottom-up and top-down auditory processing as well as the better performance of musicians in SPIN, Slater, et al. [ 91 ] used a long-term music training (2 years) to improve children’s ability to understand speech in noisy environments. Jain, et al. [ 92 ] performed an 8-day music training for adults aged 18-25 years and observed an improvement in SPIN. It has also been shown that short-term music training in the elderly improves neural speech coding and facilitates SPIN [ 93 ]. Jayakody [ 94 ] examined the effects of a computer-based music perception training program and concluded that this program improved music comprehension and speech perception in people using cochlear implants or hearing aids, more significantly in those who were using hearing aids.

Speech-in-noise training

Speech-in-noise training (SINT), which focuses on improving listening skills in noisy environments, is an important component of an auditory training program for the hearing impaired and a wide range of individuals with hearing impairment, learning disabilities, and LD [ 95 ]. Various speech materials, including syllables, words (monosyllabic and bisyllabic), sentences, phrases, and texts, at specific SNRs can be used in SINT. Such methods, which were first described by Katz and Burge [ 96 ] employed noise desensitization techniques to improve SPIN. This method increases the level of noise tolerance and memory and, as a result, improves SPIN. This technique helps participants desensitize to noise and develop strategies to obtain more information about auditory signals in difficult-to-hear situations [ 56 ]. This training program involves listening to speech stimuli at different levels of noise, because exposure to a gradually increasing level of noise reduces the effect of noise. In this program, the words are first presented in silence and then in noise, and the SNR is gradually increased from easy (e.g., +12 or +15 dB) to difficult (0 dB or negative SNRs) conditions [ 97 ]. Masters propose that a treatment hierarchy begins with white noise and ends up with the most problematic type of noise for the client. It is also suggested that such interventions take into account the type of noises that one is more exposed to in the surrounding environment [ 98 ]. Several studies have reported the effectiveness of noise speech training, particularly the noise desensitization method. According to Jutras, et al. [ 99 ], children with APD exhibited a significant increase in noise tolerance after receiving noise-specific speech training. They investigated how SINT affected speech perception test scores, electrophysiological criteria, and listening and living habits in the child’s real-life conditions and environment. Speech perception test scores and electrophysiological components improved due to this rehabilitation. Furthermore, improvement was not limited to noisy environments but it happened in other challenging situations of everyday life [ 99 ]. Kumar, et al. [ 95 ] designed a computerized tasks for speech in noise training and evaluated its effectiveness using auditory processing behavioral tests and auditory electrophysiological responses. This computer-based program (module) included teaching words in noise, in which single and three-syllable words were presented as target words in the presence of spoken noise and multi-talker babble at various SNRs ranging from +20 to -4 dB. The performance of experimental children in speech noise and auditory processing was better than the control group. In addition, after training, the range of evoked responses with high latency in silence and noise decreased significantly. Their study found that a targeted program of SINT improved auditory behavioral skills and electrophysiological responses. Another study in 2021 investigated the effects of a noise word training program using monosyllabic words in the presence of spoken and multi-speaker noise in the SNR range of +20 to 4 dB on processing and cognitive skills (working memory) of 20 children with APD (10 children in the experimental group and 10 children in the control group). The results of this study showed that SINT significantly improved the mean SPIN score, temporal processing (gap in noise test and duration pattern), and the backward digit’s span score [ 100 ].

To design speech tasks in noise, it is recommended that noise be used in conjunction with the speech signal in a systematic manner, beginning with energetic noise and progressing to informational noise. Energetic masking is associated with noise that has a high-frequency compression and no gap between the energy spectrums produced, such as white noise. In this case, it seems that the neural networks in charge of signal and noise processing are sensitive to the all energy level of the signal and noise, and thus the signal with a slightly higher absolute energy level than the noise is easily detected. Speech comprehension in a noisy environment will be possible with lower SNRs and the use of pitch and spatial cues. The difficulty and the challenging level of the auditory environment are controlled and adjusted in this type of test based on SNR. Providing speech in the presence of informational masking, on the other hand, targets a different level of ability of the neural networks responsible for signal processing in noise. Distracting noises of the signal type are referred to as informational masking. When speech is in the speech noise, a person’s ability to understand usually suffers. It is preferable to begin with multiple speech streams and end up with two speech streams (from easy to hard). In the presence of informational noise, the auditory system typically uses signal monitoring in the ears at any frequency and at any time, according to theories of speech signal detection. In other words, it is assumed that our auditory system searches for target signal among the noise gaps or tries to collect the target signal from the better ear with higher SNR, an approach which is known as cross-ear dip-listening [ 101 - 104 ].

Difficulty with SPIN is a common condition affecting all children and adults with central hearing impairment, children with LD, ADHD, autism, and hearing loss, and the elderly. Each person with this problem suffers from a defect in one or more of the underlying neural mechanisms of SPIN, which can be improved through appropriate training programs. Pitch training, spatial training, temporal training, phoneme training, functional memory training, musical training, and SINT are all used to improve SPIN. Pitch training is based on fundamental frequency and harmonics. Changing the fundamental frequency is one of the methods to improve pitch differentiation based on vowel differentiation at various fundamental frequencies. Mistuning complex tone harmonics and distinguishing tuning stimuli from mistuning from the base of harmonic training. Localization training involves simulating the space environment with headphones and investigating localization disorders in various ITDs. The detection of the number of stimuli, pitch stimuli, duration pattern, number of meaningless speech stimuli in noise, and the gap in noise is the central part of temporal training, whereas phoneme training focuses on meaningless syllables of VCV and CVC. Memory training is performed for patients with poor memory using simple stimuli, such as numbers and letters. Music training has been shown to have a significant effect on SPIN by improving the reception of acoustic stimuli and involving top-down processes of attention and memory. Finally, SINT using stimuli, such as syllables, words, and sentences, in energetic and informational background noise has been shown to improve SPIN.

Conflicts of Interest

The authors have no financial conflicts of interest.

Author Contributions

Conceptualization: Nasrin Gohari, Zahra Hosseini Dastgerdi. Data curation: Nasrin Gohari, Zahra Hosseini Dastgerdi. Formal analysis: Nasrin Gohari, Zahra Hosseini Dastgerdi. Investigation: all authors. Methodology: Nasrin Gohari, Zahra Hosseini Dastgerdi, Nematollah Rouhbakhsh. Project administration: Nasrin Gohari, Zahra Hosseini Dastgerdi. Supervision: Nasrin Gohari, Zahra Hosseini Dastgerdi. Validation:Nasrin Gohari, Nematollah Rouhbakhsh, Sara Afshar, Razieh Mobini. Visualization: Nasrin Gohari, Nematollah Rouhbakhsh, Sara Afshar, Razieh Mobini. Writing—original draft: all authors. Writing— review & editing: Nasrin Gohari, Zahra Hosseini Dastgerdi. Approval of final manuscript: all authors.

Understanding Speech-to-Noise Ratio and Its Impact on Your App

The speech-to-noise ratio (SNR) is a measure of unwanted noise in an audio stream relative to recognizable speech. The SNR can negatively affect system performance by limiting operating range or affecting receiver sensitivity. Understanding how to calculate and manage this will help you create a robust, accurate system for real-life situations.

What is speech-to-noise ratio?

The speech-to-noise ratio (SNR) measures the percentage of unwanted noise in an audio stream relative to recognizable speech. This means that the ability to hear and understand speech when there’s background noise is dependent on the SNR.

SNR is an inconvenient feature because of its impact on system performance, like limiting its operating range or affecting the receivers’ sensitivity. It’s random and unpredictable, with no pattern, constant frequency, or amplitude. You can take several measures to reduce SNR, but you can’t eliminate it completely.

An example of speech-to-noise ratio

Let’s assume two people are having a conversation when really loud construction work begins nearby. The construction work noise is 50dB, making the conversation difficult to hear.

If one person is whispering at 30dB, it’ll be hard for the other person to hear them. However, if the other person shouts at 80dB, their voice would be louder than the 50dB produced by the construction work, and both people would hear each other.

So in this case, if:

Shouting = speech (80DdB) Construction work =  noise (50dB)

then: SNR = 30dB.

How to calculate the speech-to-noise ratio

To assess and then optimize the SNR you need to be able to calculate what it is. As both speech and noise signals are random and change their statistical parameters over time, it is difficult to find a single mathematical formula abstracting the classification between them. That said, here’s a good one from the International Computer Science Institute (ICSI) in Berkeley, California:

SNR_dB = 20.log10(S_rms / N_rms)

where S_rms is the root-mean square of the speech signal (without any noise present) i.e. sqrt(1/N*sum(s[n]^2)) , and N_rms is the root-mean square level of the noise without speech.

This is equal to:

SNR_dB = 10.log10(S_e / N_e)

where S_e is the total energy of the speech i.e. sum(s[n]^2) etc. — the global SNR.

This source is an FAQ, and it goes on to explain why SNR is tricky to calculate due to the non uniform nature of the speech and the solution of finding a definition for speech SNR that does not vary when silence is added to the noise. The actual level of noise added to achieve a given global SNR depends on the amount of padding added to (or, in general, silence present in) the speech example. If you don’t take into account this ambiguity then confusion will reign! The shape of the noise spectrum also plays havoc on the calculations too. For a deep dive on the calculation, there is a detailed technical paper within the source which makes for very interesting reading.

You can also calculate SNR from a single stream of audio. In this scenario you won’t have access to speech and noise signals separately and so you have to estimate the noise in the time and frequency domains of the channel.

What types of noise impact SNR and system performance?

There are two sources of noise that affect SNR: external and internal.

External source

External source noise is either natural or man made. Common examples include:

• Atmospheric noise from irregularities in the atmosphere, like lightning discharges in thunderstorms.
• Extra-terrestrial noise, like solar noise and cosmic noise.
• Industrial noise.

This is the worst kind of SNR because you can’t completely eliminate it — but you can manage it. The best way of doing this is to avoid the noise affecting the speech signal in the first place.

Internal source

Internal source noise is caused by the receiver components during functioning. Common examples are:

• Thermal agitation noise, like Johnson noise or Electrical noise.
• Shot noise from the random movement of electrons and holes.
• Transit-time noise during transition.
• Miscellaneous noise, like flickers, resistance effect, and mixer-generated noise.

Internal source noise can be quantified. A proper receiver design may reduce its impact.

What is the industry standard?

Conventional speech recognizers are much more sensitive than human listeners. The certain SNR value for speech depends on the application. In addition, the type of noise is also relevant. For example, competing background talking or babble is the most disruptive because it matches the spectral distribution (and modulation dynamics) of the target speech.

A useful point of reference for the range in SNR is from about 30dB (barely audible noise) up to almost -5dB (noise overwhelms the original clean signal). However, the relative difference between whispering and moderate speech is >20 dB and increased word error rates are typically seen at 20dB SNR.

A SNR of >30dB is considered clean speech. Listeners will barely notice anything better than 20dB, and intelligibility is still pretty good at 0dB SNR (this is where speech energy and noise energy are the same).

How can SNR impact the accuracy of your speech recognition?

A low level of SNR will decrease how accurately your system can recognize speech. You’ll want a robust SNR in adversely noisy conditions so that the automatic speech recognizer can also be used in real-life situations, like in a multi-person meeting.

Noise limits the systems’ operating range

Noise places a limit on the weakest signal that can be amplified. It does so indirectly and in the following way: the oscillator in the mixer circuit may limit its frequency due to noise, and because a system’s operation depends on the operation of its circuits, the noise limits the smallest signal that a receiver is capable of processing.

Noise affects the receivers’ sensitivity

There’s a sensitive balance between the minimum amount of input signal necessary and the specified quality output. If noise will affect the sensitivity of a receiver system, it will also eventually affect the output.

How to deal with SNR early on so it doesn’t come back to bite you?

It’s really important to measure the SNR early during the development process, because if you discover later on that it’s high in your audio samples then you can’t blame the problem on the speech-to-text vendor. Here are two ways you can manage this:

Signal Compensation

One way to deal with this early on in the process is by way of Signal Compensation, which means removing or reducing noise effects in the preprocessing stage (i.e. prior to feature extraction and the recognition process).

The goal is to transform the noisy signal to resemble clean speech and improve the speech signal quality. You do this with speech enhancement methods, which are used as a front end for the speech recognizer.

Spectral subtraction (SS), Wiener filtering, and model-based speech enhancement, are widely used instances of this approach. SS is simple and easy to implement. Despite its low computational cost, it’s very effective where the noise corrupting the signal is additive and varies slowly with time. This paper explores speech enhancement by spectral subtraction and with Wiener filtering, both of which appear to work well in situations where there is additive, stationary noise, commonly referred to as white noise (broadband noise, like tape hiss), colored noise, and different types of narrowband noises.

Noise Injection Theory, or noisy training

Another option you can use to tackle this issue, once identified, is a noisy training approach. By intentionally and randomly injecting moderate noises into the training data, more generalizable deep neural network (DNN) models can be learned.

Noisy training for DNNs requires samples of noise signals from some real-world recordings, which you then mix with the original training data. This can also be referred to as “noise injection” or “noise corruption.” You can use this noise-corrupted speech data as part of the usual DNNs training.

There are two benefits to this approach: Firstly, the noise patterns within the introduced noise signals can be learned and therefore compensated for in the inference phase; and secondly, the disturbance introduced by the injected noise can improve the general capability of the resulting DNN.

What to prioritize when looking for an API platform provider

So, we now know that you need a robust speech recognition system, and one that can handle a higher SNR is going to be up there with the best. When looking for a provider, be sure to make this feature a priority when assessing your options.

Symbl.ai provides a robust API. Learn more about what Symbl.ai can offer here .

Additional reading

For a deeper dive into the topics raised above, the below links are full of good detail:

• Study of different types of noise and its effects in communication systems 
• How is the SNR of a speech example defined?
• Estimation of Noise Spectrum and its Application to SNR-Estimation and Speech Enhancement 
• Noisy training for deep neural networks in speech recognition
• Likelihood-maximizing-based multiband spectral subtraction for robust speech recognition

The writing team at Symbl.ai

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• Signs and Symptoms

About Noise-Induced Hearing Loss

What to know.

• Noise is all around us — at school, at home, and everywhere in between.
• Noise is a significant source of hearing loss.
• Prevention and early detection of hearing loss due to noise are important.
• Recognize signs of hearing loss and get your hearing tested if you are at risk.

Hearing loss can occur:

• in children and affect communication, language, and social skills.
• in occupational settings and result in permanent injury for workers.
• due to loud noise near the ear or repeated exposure to loud noise.

Exposure to loud noise can lead to noise-induced hearing loss. Noise-induced hearing loss can be caused by participating in activities that produce harmful sound levels or by repeated exposures to loud sounds.

Some examples of noisy activities include:

• Watching summer fireworks on the 4th of July
• Mowing the lawn
• Using power tools
• Watching a sports game and cheering on your favorite team
• Attending a concert

Recognize early signs of noise-induced hearing loss and take steps to protect your hearing.

Loud Noises Can Cause Hearing Loss

Learn about causes, signs, and prevention of noise-induced hearing loss.

Nuns of Benedictine College Condemn Harrison Butker and Say His Graduation Speech 'Fostered Division'

“The sisters do not believe that Harrison Butker’s comments represent the Catholic, Benedictine, liberal arts college that our founders envisioned," the statement read

The fallout from Harrison Butker’s controversial graduation speech continues to rage on, as nuns from Benedictine College join the list as the latest to condemn his words.

In a lengthy statement posted to Facebook, the nuns from Benedictine College publicly called out Butker’s speech, which was delivered during the college’s 2024 commencement ceremony.

“The sisters of Mount St. Scholastica do not believe that Harrison Butker’s comments in his 2024 Benedictine College commencement address represent the Catholic, Benedictine, liberal arts college that our founders envisioned and in which we have been so invested.” the statement read. 

The nuns added that Butker’s words were divisive and the assertion that women should only be homemakers is false.

“Instead of promoting unity in our church, our nation, and the world, his comments seem to have fostered division. One of our concerns was the assertion that being a homemaker is the highest calling for a woman…Our community has taught young women and men not just how to be 'homemakers' in a limited sense, but rather how to make a Gospel-centered, compassionate home within themselves,” the statement said.

Related: Chiefs' Harrison Butker Criticized for Graduation Speech Attacking Working Women While Quoting Taylor Swift

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“We reject a narrow definition of what it means to be Catholic,” the statement read, calling out Butker’s words surrounding the idea that women who are stay-at-home moms are the true definition of what it means to be rooted in their faith.

Concluding the statement, the nuns highlighted that despite Butker’s divisive words, Benedictine College wants to be known as welcoming and celebrating diversity.

“We want to be known as an inclusive, welcoming community, embracing Benedictine values that have endured for more than 1,500 years and have spread through every continent and nation. We believe those values are the core of Benedictine College,” the statement read.

This latest rejection of Butker’s speech follows other high profile figures and organizations slamming his words, including Maria Shriver , the NFL and former Kansas City commissioner Justice Horn. Some have supported Butker, including Tavia Hunt, the wife of Chiefs owner Clark Hunt , who posted on Instagram that encouraging women to stay at home is "not bigoted."

Related: Maria Shriver Slams Harrison Butker After Controversial Graduation Speech: 'Demeaning to Women'

During his nearly 20-minute speech, Butker spoke about a host of social issues, including the “diabolical lies told to women” about working rather than becoming homemakers, and offered his take on abortion, in vitro fertilization (IVF) and surrogacy. He also said Pride Month represented "deadly sins."

At one point, seeking out the men in the audience, the Chiefs kicker advised them to “be unapologetic in your masculinity," and to "fight against the cultural emasculation of men."

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Read the original article on People .

At revered Black school, Biden leans into faith and tells grads he hears voices of dissent

ATLANTA – President Joe Biden on Sunday warned graduates at one of the country's most revered African American academic institutions of "extremist forces aligned against the meaning and message of Morehouse" College in a commencement address that sought to lay out the stakes of the 2024 election.

"Graduates, this is what we're up against," Biden said during a 27-minute speech that leaned heavily into themes of faith and democracy in an appeal to Black voters. "They peddle a fiction, a character about what being a man is about − tough talk, abusing power, bigotry. Their idea of being a man is toxic."

"But that's not you. It's not us," he said.

Biden's remarks to the 414 graduates at Morehouse , an all-male historically Black college in Atlanta, came as he is struggling to unite Black voters , particularly Black men, around his candidacy. Many Morehouse students and faculty criticized Biden's participation when it was announced because of his support for Israel's war in Gaza.

"In a democracy, we debate dissent about America's role in the world. I want to say this very clearly: I support peaceful, nonviolent protest," Biden said on Sunday in response to the complaints. "Your voices should be heard. I promise you, I hear them."

Prep for the polls: See who is running for president and compare where they stand on key issues in our Voter Guide

No major disruptions, but peaceful protests target Biden

Although there were no major disruptions during Biden's speech, a few students walked out when Biden received an honorary Morehouse degree. More than a dozen graduates and at least three faculty members wore keffiyehs, while one student entered the ceremony draped in a Palestinian flag.

As Biden delivered his address, at least one female faculty member stood in the opposite direction, her fist raised, in a sign of protest.

Biden, wearing a maroon gown at the outdoor ceremony, said his administration is "working around the clock for more than just one cease-fire," but also to "bring the region together." He reiterated his support for a two-state solution in which Israelis and Palestinians live in peace.

"This is one of the hardest, most complicated problems in the world. There's nothing easy about it," Biden said. "I know it angers and frustrates many of you, including my family, but most of all, I know it breaks your heart. It breaks mine as well."

Biden added that leadership is about "fighting through the most intractable problems" to "find a solution by doing what you believe is right, even when it's hard and lonely."

About a mile away, pro-Palestinian protesters held a rally organized under the banner of "Say No to Genocide Joe Speaking at Morehouse." Morehouse's valedictorian also raised Israel's war in Gaza during his remarks before Biden took the lectern.

"It is my stance as a Morehouse man – nay as a human being – to call for an immediate and the permanent cease-fire in the Gaza Strip," graduating senior DeAngelo Jeremiah Fletcher said, with Biden sitting just steps behind him. Biden applauded in response.

Biden touts record with Black voters

Polling shows Biden is vastly underperforming his 2020 performance among Black voters, a reliably Democratic constituency, as some drift to Donald Trump, the former president and presumptive Republican nominee.

A New York Times/Siena College poll of six battleground states, including Georgia, found Biden has support from 60% of Black voters and Trump, while Trump is backed by 20% of Black voters. Biden won Black voters in the 2020 election by a 87%-12% margin, according to exit polls.

Ahead of Biden's arrival, Anwar Karim, a sophomore studying film at Morehouse and a member of Atlanta University Center Students for Justice in Palestine, told USA TODAY he was disappointed in his school’s choice of commencement speaker. He also decried Morehouse’s decision to award Biden an honorary degree, which is typically awarded to the school’s commencement speaker after a faculty vote.

“Morehouse College is dedicated to producing men of consequence who lead lives of service and leadership, and I just have to beg the question, when it comes to Biden, what is an example of his leadership?” Karim said Friday.

Biden commits to showing 'democracy is still the way'

In his speech, Biden touted his presidency as one that has delivered to Black Americans, pointing to efforts to invest in Black families and communities, cut child poverty, expand work opportunities, reduce prescription drug prices and cut student loan debt. He called out the "poison of white supremacy" and "systemic racism."

He said he is committed to "show that democracy, democracy, democracy is still the way," even in the face of inequality for Black Americans.

"What is democracy if Black men are being killed in the street? What is democracy if the trail of broken promises still leave Black communities behind?" Biden said. "What is democracy if you have to be ten times better than anyone else to get a fair shot? Most of all, what does it mean, as you've heard before, to be a Black man who loves his country even if it doesn't love him back in equal measure?"

Biden railed against new voting restrictions in Georgia and the "constant attacks on Black election workers." He also said those who stormed the U.S. Capitol on Jan. 6, 2021 "are called patriots by some," a clear reference to Trump.

“Not in my house," Biden said.

In the days leading up to his Morehouse visit, the White House focused on Black outreach. Biden met on Thursday with plaintiffs of the landmark Brown v. Board of Education Supreme Court decision, on the 70th anniversary of the dismantling of the "separate but equal" precedent. On Friday, Biden met with leaders of the "Divine Nine" HBCU sororities and fraternities.

More: In a nod to history, Biden meets with Brown v. Board of Education families

In Atlanta on Saturday, Biden spoke to Morehouse alumni and others at a campaign event at Mary Mac's Tea Room. "The fact is, this election, lots at stake, lots at stake. It's not about me. It's about the alternative as well," Biden said. "My opponent's not a good loser, but he is a loser."

Introducing Biden, Morehouse President David Thomas said, "No administration in history, since the inception of historically Black colleges and universities, has invested more in our institutions than the Biden administration."

"And if you look at his policies, it is very clear that those investments are not charity," Thomas said.

Biden, 81, closed his remarks with a reference to his age, a liability that has hung over his reelection. When he started his political career, Biden said he was told he was "too young." Now he hears he's "too old."

"Whether you're young or old, I know what endures: The strength and wisdom of faith endures. And my challenge to you is to still keep the faith as long as you can," Biden said. "Together we're capable of building a democracy worthy of our dreams."

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Biden delivers Morehouse commencement speech as some on campus express pro-Palestinian messages

ATLANTA — President Joe Biden delivered the commencement address at Morehouse College on Sunday morning, his most direct engagement with college students since the start of the Israel-Hamas war and a key opportunity for him to engage with a group of voters that data suggests is softening on him: young, Black men.

In his remarks, Biden ticked through his administration's policies that he said have aided Black Americans, including a record $16 billion in new aid for historically Black colleges and universities.

And, in a nod to the pro-Palestinian sentiment among Morehouse students and faculty, Biden reiterated his calls for an immediate cease-fire in Gaza, more humanitarian aid in the region and support for a two-state solution that would lead to the creation of a Palestinian state.

“We’ve been working on a deal as we speak. Working around the clock to lead an international effort to get more aid into Gaza, rebuild Gaza. I’m also working around the clock for more than just one cease-fire. I’m working to bring the region together. Working to build a lasting, durable peace,” he said.

As Biden spoke, roughly six students in the crowd sat turned away from him. Though Biden did not reference the action directly, his remarks touched on the “anger and frustration” felt by many Americans over the war, including by members of his own family.

“I know it breaks your heart. It breaks mine as well,” Biden said. “Leadership is about fighting through the most intractable problems. It’s about challenging anger, frustration and heartbreak. To find a solution. It’s about doing what you believe is right, even when it’s hard and lonely.”

Following the speech, Morehouse President David Thomas praised Biden for a “thought-provoking speech” he said was reflective of the president “listening.”

“You spoke to the hard issues confronting our nation and the world at this moment,” Thomas said before conferring an honorary doctorate degree onto Biden.

No significant, disruptive protests materialized, but some students and faculty members still expressed their support for Gaza during the ceremony.

Pro-Palestinian demonstrations began even before Biden took the stage Sunday morning. As graduates and faculty entered the ceremony, at least eight students and three staff members wore pro-Palestinian garb, some adorned in Palestinian flags and others wearing keffiyeh scarves.

An opening prayer by the Rev. Claybon Lea Jr. urged those in power to be “accountable for valuing human life” across the globe.

“Whether they live in Israel or Palestine, Ukraine or Russia, the Congo or Haiti, God give us men that will value life and call us to accountability. Give us men who require all of us to live the golden rule and even follow the edicts of that Palestinian Jew named Jesus,” Lea said as Biden sat inches behind him.

In the most direct call to action of the ceremony, valedictorian DeAngelo Jeremiah Fletcher concluded his remarks by calling for an immediate cease-fire in Gaza, framing his decision to speak on the conflict as a moral duty in line with the legacy of fellow Morehouse alumnus Martin Luther King Jr.

“It is important to recognize that both sides have suffered heavy casualties in the wake of Oct. 7,” Fletcher said. “From the comfort of our homes, we watch an unprecedented number of civilians mourn the loss of men, women and children while calling for a release of all hostages. For the first time in our lives, we’ve heard the global community sing one harmonious song that transcends language and culture. It is my stance as a Morehouse man named as a human being to call for an immediate and a permanent cease-fire in the Gaza Strip.”

As Biden took the stage, graduating students remained seated and silent, even as older alumni nearby cheered.

And during his remarks, faculty member Samuel Livingston held up the flag of the Democratic Republic of Congo, in an effort to bring attention to ongoing conflict in the region.

Sebastian Gordon, a graduating senior from Washington, D.C., was satisfied with Biden's remarks. “I know one concern that my class had was actions and words didn’t line up,” Gordon told NBC News. “I’m happy with his words that he said. I’m just going to continue to watch to make sure his actions line up with that.”

The protests during the commencement were largely peaceful, following instructions Thomas, the school president, gave to faculty and students across at least three meetings: The right to protest would be honored as long as they’re not disruptive.

Ahead of the commencement, Thomas told CNN that though he would not ask police to intervene should protests occur during Biden’s remarks, he would immediately bring the commencement to a halt.

“I have also made a decision that we will also not ask police to take individuals out of commencement in zip ties. If faced with the choice, I will cease the ceremonies on the spot if we were to reach that position,” Thomas said.

Even the most vocal student protesters at Morehouse predicted that protests during the commencement ceremony would likely not be disruptive, partially due to the volatility a police response would likely incite.

“I think that whatever happens on Sunday on the part of the people and the people who want to see some change is going to be peaceful,” sophomore Anwar Karim said. “I don’t see it erupting like it has at some of the other campuses, because we at HBCUs here are also just mindful of the fact of how interactions with police often go.”

White House press secretary Karine Jean-Pierre said Friday that Biden spent several days working on the speech, tapping into a brain trust of senior advisers, including some Morehouse alums, to craft his message to the 415 Black men graduating from the school.

Biden previewed the tone of his remarks during a speech Thursday to commemorate the 70th anniversary of the Supreme Court’s Brown v. Board of Education decision.

“Morehouse was founded after our nation’s Civil War to help prepare Black Americans who were formerly enslaved to enter the ministry, earn an education and usher them from slavery to freedom,” Biden said before announcing $16 billion in new investments for historically Black colleges and universities. “The founders of Morehouse understood something fundamental. Education is linked to freedom. Because to be free means to have something that no one can ever take away from you.”

Biden’s speech at Morehouse came against the backdrop of protests on college campuses nationwide over his handling over the war in Gaza, with many students and faculty members voicing opposition to the White House’s continued financial and military support for Israel. Some at Morehouse hoped Biden would speak directly to those concerns during his commencement remarks.

“I hope that we don’t get boilerplate language. I hope that we get something we haven’t heard before. I hope that his ethical, moral conscience trump any politics,” Morehouse professor Stephane Dunn said at a protest Friday.

Morehouse has also had pro-Palestinian protests on campus, though the HBCU did not see the same scale or escalation of demonstrations as some larger universities.

The school’s decision to host Biden as commencement speaker and award him an honorary doctorate degree almost immediately sparked protests among faculty and students, some continuing into the days leading up to the commencement ceremony.

“This is one big distraction on a day to celebrate the class of 2024 following Covid-19, but this is also an opportunity for students to make their voices heard during a time of increasing war and genocide in the Middle East,” Morehouse senior Calvin Bell said in reaction to Biden’s visit.

“We as students, faculty and alums who are standing on the right side of history do not stand with Biden,” Karim said. “We do not align ourselves with all of the clear and avid support that he’s had for a genocidal campaign on the part of the Israelis for the last over 200-plus days.”

Most recently, Morehouse faculty were split over the decision to award Biden an honorary doctorate degree at the ceremony. A letter circulated among staff members in protest of the decision got more that two dozen signatures in support, and the vote to award the degree passed 50-38, with roughly 12 faculty members abstaining.

The White House deployed its allies to Morehouse, both formally and informally, to assuage concerns and lower tensions over Biden’s visit.

Steve Benjamin, who heads the White House Office of Public Engagement, met with a small group of Morehouse students and faculty this month following a push from the school’s leadership for “direct engagement” from the White House.

During the meeting, some students expressed concerns about Biden overshadowing their graduation, while others implored Benjamin to ensure Biden’s speech doesn’t double as a campaign stump speech — frustrated with the idea of the commencement address being a vehicle for Biden to bolster support among Black voters.

That sentiment was shared by other Morehouse students critical of Biden’s visit.

“I don’t think it’s a coincidence that he only accepted the invitation after Trump was already in [Atlanta’s] West End, trying to make gains and failing to make gains with our students here,” Morehouse student Malik Poole said at a campus protest ahead of Biden’s visit. “And this is coming at a time where voters of color are fleeing from Biden at record pace.”

But still, Biden’s Morehouse visit came amid a concerted effort by his administration and campaign in the past week to sharpen his message to Black voters .

On Thursday, Biden met with plaintiffs and their family members from the historic Brown v. Board of Education case. The following day, he met with leaders of the Divine Nine, a group of historically Black sororities and fraternities, alongside Vice President Kamala Harris, a member of the Alpha Kappa Alpha sorority herself. During his trip to Georgia, Biden attended an event Saturday focused on engaging Black voters. And following his commencement address, Biden will close out the weekend by delivering the keynote address at the NAACP Freedom Fund dinner in Detroit, where he plans to tout his administration’s accomplishments for Black Americans.

As data suggests that Black voters — particularly young Black voters — are souring on Biden, some at Morehouse recognized the “opportunity” Biden had to make his case to members of that voting bloc during his address.

“If you want ... these students to vote in the fall for you, you have to give them something that shows that you are hearing them,” Dunn said. “That you are trying to do something we haven’t heard about. This is the opportunity.”

Nnamdi Egwuonwu is a 2024 NBC News campaign embed.