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Converting Speech to Text with Spark NLP and Python

Hear Me Out: How to Convert Your Voice to Text with Spark NLP and Python

Automatic Speech Recognition — ASR (or Speech to Text) is an essential task in NLP that can create text transcriptions of audio files. The open-source NLP Python library by John Snow Labs implemented two models for ASR: Facebook’s Wav2Vec version 2.0 and HuBERT, which achieve state-of-the-art accuracy on most public datasets. You learn how to use the library to extract texts from a given audio file and apply Named Entity Recognition to the extracted text.

Introduction

Automatic Speech Recognition (ASR), or Speech to Text, is an NLP task that converts audio inputs into text. It is helpful for many applications, including automatic caption generation for videos, dictation to generate reports and other documents, or creating transcriptions of audio recordings.

To perform this task, modern models use transformers-based deep learning models, and out of those, we have:

• Wav2Vec 2.0, created and shared by a Facebook researcher on wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli
• HuBERT, also proposed by Facebook on HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed

These models use the encoder-decoder architecture based on Transformers. By the end of this post, you will have a better understanding of ASR and how to use Spark NLP to create pipelines, process audio files, and extract their texts at scale.

Both Wav2Vec and HuBERT models consist of an encoder-decoder architecture. They encode the audio (converted in arrays of float numbers) into a dense representation and then decode this representation using attention-based (Transformers) models of NLP to generate the text.

The main differences between Wav2Vec 2.0 and HuBERT are how they process the audio input and the loss function to measure the performance of the outputs and backpropagate the errors during training.

While Wav2Vec 2.0 transforms the audio using the quantization technique with a Gumbel Softmax sampling to determine the candidate words, the HuBERT model uses the K-means algorithm to cluster the audio inputs and create embeddings of them that are used in the prediction step. As for the loss function, the Wav2Vec 2.0 uses the CTC loss together with a Diversity loss, and the HuBERT model uses cross-entropy.

After learning about the models, let’s see how to use them in Spark NLP. But wait, what is Spark NLP?

Introduction to Spark NLP

Spark NLP is an open-source library maintained by John Snow Labs . It is built on top of Apache Spark and Spark ML. It provides simple, performant & accurate NLP annotations for machine learning pipelines that can scale quickly in a distributed environment.

To install Spark NLP, you can simply use any package manager like conda or pip . For example, using pip, you can run pip install spark-nlp . For different installation options, check the official documentation .

Spark NLP processes the data using Pipelines , a structure that contains all the steps to be run on the input data the same way Spark ML does.

Example pipeline in Spark NLP

Each stage of the pipeline is created by an annotator that uses one or more of the previous information to create a new annotation . Each annotator can be of two types, AnnotatorModel which can be used to make predictions based on pretrained models and AnnotatorApproach which can be used to train new custom models. Pretrained models can be found in the NLP Models Hub.

As part of the spark ecosystem, before using the library, we need to start a spark session, which can be done by:

Pre-processing audio files

To extract text from audio files, we must first load the audio in memory as an array of float numbers and then send the data to spark for processing. Let’s see how to use the library librosa to do that.

First, we make sure that the library is installed.

For this post, we will use a sample audio file available at John Snow Labs’ public database

We use librosa to load the audio file as a numpy array and then convert the data into a list of float numbers (Spark NLP annotators are not compatible with numpy.float32 data type at this moment):

We used a sampling rate of 16 kHz, which is enough for our purposes and is commonly used in ASR applications. The .load() method can resample the audio to fit our needs by passing the parameter. sr=16000 . Next, we will send this information to a spark data frame:

Now we have a spark data frame with one column named audio_content containing the array of the audio input. This is the required format to be processed by the ASR models.

Creating the Spark NLP pipeline for processing

Next, we can create a pipeline for processing the audio file and extracting the text. Let’s import the required modules and annotators to do that:

We need the AudioAssembler annotator to transform the audio array into an AUDIO type annotation, which will be used by any of the Wav2Vec2ForCTC or HubertForCTC annotators to extract its text. We can use both models in the same pipeline without duplicating the audio annotations. This is helpful when experimenting with different models for quick comparison.

Currently, Spark NLP has more than 2,600 pretrained models for the Wave2Vec 2.0 model while having only one pretrained model for the HuBERT model. The available models can be found in the NLP Models Hub . We will use similar models for a fair comparison, trained in the LibriSpeech dataset.

With this, the pipeline is defined, having the class Pipeline from Spark ML. To make predictions, we need first to fit the model to data, obtaining a PipelineModel . As we have only pretrained stages in our pipeline, no training will be performed, and this will be only a formality.

Next, we will run the model on the same audio file and examine its results.

Extracting text from audio with Spark NLP

To extract the text, we can simply run the obtained PipelineModel on the spark data frame we created. Then we can display the obtained text using the .show() method.

We can put the obtained texts together for better visualization (I added extra spaces for better comparison):

We can see that for this example file, both models achieved equivalent results.

One-liner alternative

In October 2022, John Snow Labs released the open-source johnsnowlabs library that contains all the company products, open-source and licensed, under one common library.

This simplified the workflow, especially for users that work with more than one of the libraries (e.g., Spark NLP + Healthcare NLP ). To use the library, you first need to install it with your preferred package manager:

Then we can import any of the libraries by using the corresponding modules:

NOTE: when using johnsnowlabs library, make sure you initialize the spark session with the configuration you have available. Since some libraries are licensed, you may need to set the path to your license file.

If you want to use the open-source libraries only, you can start the session with spark = nlp.start(nlp=False) . The default parameters for the start function include using the licensed Healthcare NLP library with nlp=True , but we can set that to False and use all the resources of the open-source libraries such as Spark NLP, Spark NLP Display, and NLU.

Then, to run a Wav2Vec2ForCTC annotator directly on the audio file, we can run the following command:

Which will return a Pandas data frame with two columns: audio_content containing the audio array of float numbers and text containing the extracted text . Simple as that!

Fast inference with LightPipelines

We can use Spark NLP’s LightPipeline to run fast inference directly on text (or list of text) instead of using spark data frames.

Let’s check how to do that.

For audio data (list of float numbers), we should use the .fullAnnotate() method of LightPipeline . The .annotate() method is not currently supported for audio inputs.

The .fullAnnotate() method returns a list containing each stage annotation with all metadata included.

Easy as that!

Application: Identifying Named Entities in the transcripts

Now that we have the transcripts obtained from the audio file, we can use other Spark NLP annotators to make additional NLP analyses and process them. For example, we can use pretrained Named Entity Recognition (NER) models to identify special mentions on the transcripts.

We will apply the standard preprocessing on the obtained transcripts: lowercasing the text, and removing non-letter characters (punctuation and numbers). We could go one extra mile by adding more complex steps such as spell checking and correction, removing stopwords, etc.

To use the NER model , we need both TOKEN and WORD_EMBEDDING annotations, which can be obtained by Spark NLP’s Tokenizer and BertEmbeddings annotators.

The pretrained NER model can be obtained with the NerModel annotator. We will use the onto_small_bert_L4_256 model, which was trained with the small_bert_L4_256 embedding model. We need to use the same embeddings used during the training of the NER model. Finally, we will add the NerConverter annotator that cleans the output of NER entities in a more readable way. Note that for this example, we will use the output of the Wav2Vec model, so the input column of the Tokenizer annotator is “wav2vec”.

Let’s see how to complete the pipeline:

Now we can use the NER pipeline on the previously obtained transcriptions. We will use the pyspark.sql.functions to manipulate the obtained data frame and extract the relevant information from the annotations.

Many relevant entities were correctly identified, even with spelling errors. That’s very impressive using only pretrained models freely available and ready to use at scale!

Additional resources

• ASR page on Hugging Face
• Hubert page on Hugging Face
• Automatic Speech Recognition (ASR) Software — An Introduction, by UsabilityGeek
• Documentation: HubertForCTC , Wav2Vec2forCTC
• Python Docs: HubertForCTC , Wav2Vec2ForCTC
• Scala Docs: HubertForCTC , Wav2Vec2ForCTC
• One-liner additional examples
• For other examples of usage, see the Spark NLP Workshop repository .

Try Automatic Speech Recognition

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Frequently asked questions about Speech to Text

What is speech to text.

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What are Azure AI Services?

AI Services  are a collection of customizable, prebuilt AI models that can be used to add AI to applications. There are a variety of domains, including Speech, Decision, Language, and Vision. Speech to Text is one feature within the Speech service. Other Speech related features include  Text to Speech ,  Speech Translation , and  Speaker Recognition . An example of a Decision service is  Personalizer , which allows you to deliver personalized, relevant experiences. Examples of AI Languages include  Language Understanding ,  Text Analytics  for natural language processing,  QnA Maker  for FAQ experiences, and  Translator  for language translation.

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Using NLP for Automatic Speech Recognition

Natural Language Processing (NLP) helps computers learn, understand, and produce content in human or natural language. Text/character recognition and speech/voice recognition are capable of inputting the information in the system, and NLP helps these applications make sense of this information. NLP-based systems are especially effective for augmenting both human-human communication (like language translation) and human-machine communication (like virtual assistants).

For example, in 2011, IBM Watson won over its human competitors in Jeopardy's popular US quiz show. Watson instantly became viral. Jeopardy posed significant challenges for an AI machine, unlike other board games. Watson displayed immense potential while answering complex riddles and questions on the quiz show. Watson proudly showcased its prowess in understanding languages. Watson’s victory was achieved due to its immense neural network, built over three years with researchers for Jeopardy.

After Watson’s achievement, NLP and associated AI technologies entered the consumer realm with great enthusiasm. Any business that wishes to stay ahead of its competitors in investing in AI and NLP technologies. A great example of NLP and AI applications are chatbots which can answer routine queries, help in ticketing, and offer faster issue resolutions. Businesses are even using NLP for recruitment in their business model for better employee retainment and asset assignment. 

Introduction of Speech Recognition

Speech recognition, also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, can process human speech into a written format. It’s commonly consumed for voice; speech recognition focuses on translating speech from a verbal format to a text one, whereas voice recognition seeks to identify an individual user’s voice.

There are different models in speech recognition. These can be divided into acoustic models and language models. The acoustic model is responsible for turning sound signals into a phonetic representation. The language model is responsible for housing grammar and sentence structure. These models work wonderfully well with problematic machine learning models for visible improvement. Hidden Markov models have been refined with advances for automatic speech recognition over a few decades and are considered the traditional ASR solution. 

Seeing the evolution of ASR technology, NLP is much more important than directed dialogue in the development of speech recognition systems. The typical vocabulary of an NLP ASR system consists of 60 thousand or more words. There are over 215 trillion possible word combinations if one adds a three-word sequence to it! The algorithm is designed to simulate how humans themselves understand speech and respond accordingly loosely.

For example, if one says phrases like “weather forecast”, “check my balance”, and “I’d like to pay my bills”, the tagged keywords the NLP system focuses on might be “forecast”, “balance”, and “bills”. It would then comprehend the words and context through the phrasing and not commit errors like confusing “weather” with “whether”.

Aspects of ASR with NLP

The tuning test: how asr is made to “learn” from humans.

NLP is used to train ASR through two mechanisms. The first and more straightforward is called Human “Tuning”. The second, much more advanced variant is called “Active Learning”.

Human Tuning

Human Tuning is a simple way of performing ASR training. It involves human programmers going through the logs of the different conversations of a given ASR software interface and searching at the typically used phrases that it needed to listen to, however, which it does not have in its pre-programmed vocabulary. Those phrases are then introduced to the software program to increase its comprehension of speech.

Active Learning

Active learning is a lot more sophisticated version of ASR and is explicitly being tried with NLP versions of speech recognition technology. With active learning, the software itself is programmed to autonomously research, preserve and undertake new words, therefore constantly evolving its vocabulary as it’s exposed to new methods of talking and saying things.

ASR with NLP is a topic trending to various kinds of research and innovations. Speech recognition is one of the main parts of this field. Many types of models and methods are available using existing technologies to recognize speech. Siri, Alex, and Google demonstrate what ASR and NLP have achieved thus far. 

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Natural Language Processing

Introduction.

Natural Language Processing (NLP) is one of the hottest areas of artificial intelligence (AI) thanks to applications like text generators that compose coherent essays, chatbots that fool people into thinking they’re sentient, and text-to-image programs that produce photorealistic images of anything you can describe. Recent years have brought a revolution in the ability of computers to understand human languages, programming languages, and even biological and chemical sequences, such as DNA and protein structures, that resemble language. The latest AI models are unlocking these areas to analyze the meanings of input text and generate meaningful, expressive output.

What is Natural Language Processing (NLP)

Natural language processing (NLP) is the discipline of building machines that can manipulate human language — or data that resembles human language — in the way that it is written, spoken, and organized. It evolved from computational linguistics, which uses computer science to understand the principles of language, but rather than developing theoretical frameworks, NLP is an engineering discipline that seeks to build technology to accomplish useful tasks. NLP can be divided into two overlapping subfields: natural language understanding (NLU), which focuses on semantic analysis or determining the intended meaning of text, and natural language generation (NLG), which focuses on text generation by a machine. NLP is separate from — but often used in conjunction with — speech recognition, which seeks to parse spoken language into words, turning sound into text and vice versa.

Why Does Natural Language Processing (NLP) Matter?

NLP is an integral part of everyday life and becoming more so as language technology is applied to diverse fields like retailing (for instance, in customer service chatbots) and medicine (interpreting or summarizing electronic health records). Conversational agents such as Amazon’s Alexa and Apple’s Siri utilize NLP to listen to user queries and find answers. The most sophisticated such agents — such as GPT-3, which was recently opened for commercial applications — can generate sophisticated prose on a wide variety of topics as well as power chatbots that are capable of holding coherent conversations. Google uses NLP to improve its search engine results , and social networks like Facebook use it to detect and filter hate speech . 

NLP is growing increasingly sophisticated, yet much work remains to be done. Current systems are prone to bias and incoherence, and occasionally behave erratically. Despite the challenges, machine learning engineers have many opportunities to apply NLP in ways that are ever more central to a functioning society.

What is Natural Language Processing (NLP) Used For?

NLP is used for a wide variety of language-related tasks, including answering questions, classifying text in a variety of ways, and conversing with users. 

Here are 11 tasks that can be solved by NLP:

• Sentiment analysis is the process of classifying the emotional intent of text. Generally, the input to a sentiment classification model is a piece of text, and the output is the probability that the sentiment expressed is positive, negative, or neutral. Typically, this probability is based on either hand-generated features, word n-grams, TF-IDF features, or using deep learning models to capture sequential long- and short-term dependencies. Sentiment analysis is used to classify customer reviews on various online platforms as well as for niche applications like identifying signs of mental illness in online comments.

• Toxicity classification is a branch of sentiment analysis where the aim is not just to classify hostile intent but also to classify particular categories such as threats, insults, obscenities, and hatred towards certain identities. The input to such a model is text, and the output is generally the probability of each class of toxicity. Toxicity classification models can be used to moderate and improve online conversations by silencing offensive comments , detecting hate speech , or scanning documents for defamation . 
• Machine translation automates translation between different languages. The input to such a model is text in a specified source language, and the output is the text in a specified target language. Google Translate is perhaps the most famous mainstream application. Such models are used to improve communication between people on social-media platforms such as Facebook or Skype. Effective approaches to machine translation can distinguish between words with similar meanings . Some systems also perform language identification; that is, classifying text as being in one language or another. 
• Named entity recognition aims to extract entities in a piece of text into predefined categories such as personal names, organizations, locations, and quantities. The input to such a model is generally text, and the output is the various named entities along with their start and end positions. Named entity recognition is useful in applications such as summarizing news articles and combating disinformation . For example, here is what a named entity recognition model could provide: 

• Spam detection is a prevalent binary classification problem in NLP, where the purpose is to classify emails as either spam or not. Spam detectors take as input an email text along with various other subtexts like title and sender’s name. They aim to output the probability that the mail is spam. Email providers like Gmail use such models to provide a better user experience by detecting unsolicited and unwanted emails and moving them to a designated spam folder. 
• Grammatical error correction models encode grammatical rules to correct the grammar within text. This is viewed mainly as a sequence-to-sequence task, where a model is trained on an ungrammatical sentence as input and a correct sentence as output. Online grammar checkers like Grammarly and word-processing systems like Microsoft Word use such systems to provide a better writing experience to their customers. Schools also use them to grade student essays . 
• Topic modeling is an unsupervised text mining task that takes a corpus of documents and discovers abstract topics within that corpus. The input to a topic model is a collection of documents, and the output is a list of topics that defines words for each topic as well as assignment proportions of each topic in a document. Latent Dirichlet Allocation (LDA), one of the most popular topic modeling techniques, tries to view a document as a collection of topics and a topic as a collection of words. Topic modeling is being used commercially to help lawyers find evidence in legal documents . 
• Autocomplete predicts what word comes next, and autocomplete systems of varying complexity are used in chat applications like WhatsApp. Google uses autocomplete to predict search queries. One of the most famous models for autocomplete is GPT-2, which has been used to write articles , song lyrics , and much more. 
• Database query: We have a database of questions and answers, and we would like a user to query it using natural language. 
• Conversation generation: These chatbots can simulate dialogue with a human partner. Some are capable of engaging in wide-ranging conversations . A high-profile example is Google’s LaMDA, which provided such human-like answers to questions that one of its developers was convinced that it had feelings .
• Information retrieval finds the documents that are most relevant to a query. This is a problem every search and recommendation system faces. The goal is not to answer a particular query but to retrieve, from a collection of documents that may be numbered in the millions, a set that is most relevant to the query. Document retrieval systems mainly execute two processes: indexing and matching. In most modern systems, indexing is done by a vector space model through Two-Tower Networks, while matching is done using similarity or distance scores. Google recently integrated its search function with a multimodal information retrieval model that works with text, image, and video data.

• Extractive summarization focuses on extracting the most important sentences from a long text and combining these to form a summary. Typically, extractive summarization scores each sentence in an input text and then selects several sentences to form the summary.
• Abstractive summarization produces a summary by paraphrasing. This is similar to writing the abstract that includes words and sentences that are not present in the original text. Abstractive summarization is usually modeled as a sequence-to-sequence task, where the input is a long-form text and the output is a summary.
• Multiple choice: The multiple-choice question problem is composed of a question and a set of possible answers. The learning task is to pick the correct answer. 
• Open domain : In open-domain question answering, the model provides answers to questions in natural language without any options provided, often by querying a large number of texts.

How Does Natural Language Processing (NLP) Work?

NLP models work by finding relationships between the constituent parts of language — for example, the letters, words, and sentences found in a text dataset. NLP architectures use various methods for data preprocessing, feature extraction, and modeling. Some of these processes are: 

• Stemming and lemmatization : Stemming is an informal process of converting words to their base forms using heuristic rules. For example, “university,” “universities,” and “university’s” might all be mapped to the base univers . (One limitation in this approach is that “universe” may also be mapped to univers , even though universe and university don’t have a close semantic relationship.) Lemmatization is a more formal way to find roots by analyzing a word’s morphology using vocabulary from a dictionary. Stemming and lemmatization are provided by libraries like spaCy and NLTK. 
• Sentence segmentation breaks a large piece of text into linguistically meaningful sentence units. This is obvious in languages like English, where the end of a sentence is marked by a period, but it is still not trivial. A period can be used to mark an abbreviation as well as to terminate a sentence, and in this case, the period should be part of the abbreviation token itself. The process becomes even more complex in languages, such as ancient Chinese, that don’t have a delimiter that marks the end of a sentence. 
• Stop word removal aims to remove the most commonly occurring words that don’t add much information to the text. For example, “the,” “a,” “an,” and so on.
• Tokenization splits text into individual words and word fragments. The result generally consists of a word index and tokenized text in which words may be represented as numerical tokens for use in various deep learning methods. A method that instructs language models to ignore unimportant tokens can improve efficiency.  

• Bag-of-Words: Bag-of-Words counts the number of times each word or n-gram (combination of n words) appears in a document. For example, below, the Bag-of-Words model creates a numerical representation of the dataset based on how many of each word in the word_index occur in the document. 

• Term Frequency: How important is the word in the document?

TF(word in a document)= Number of occurrences of that word in document / Number of words in document

• Inverse Document Frequency: How important is the term in the whole corpus?

IDF(word in a corpus)=log(number of documents in the corpus / number of documents that include the word)

A word is important if it occurs many times in a document. But that creates a problem. Words like “a” and “the” appear often. And as such, their TF score will always be high. We resolve this issue by using Inverse Document Frequency, which is high if the word is rare and low if the word is common across the corpus. The TF-IDF score of a term is the product of TF and IDF. 

• Word2Vec , introduced in 2013 , uses a vanilla neural network to learn high-dimensional word embeddings from raw text. It comes in two variations: Skip-Gram, in which we try to predict surrounding words given a target word, and Continuous Bag-of-Words (CBOW), which tries to predict the target word from surrounding words. After discarding the final layer after training, these models take a word as input and output a word embedding that can be used as an input to many NLP tasks. Embeddings from Word2Vec capture context. If particular words appear in similar contexts, their embeddings will be similar.
• GLoVE is similar to Word2Vec as it also learns word embeddings, but it does so by using matrix factorization techniques rather than neural learning. The GLoVE model builds a matrix based on the global word-to-word co-occurrence counts. 
• Numerical features extracted by the techniques described above can be fed into various models depending on the task at hand. For example, for classification, the output from the TF-IDF vectorizer could be provided to logistic regression, naive Bayes, decision trees, or gradient boosted trees. Or, for named entity recognition, we can use hidden Markov models along with n-grams. 
• Deep neural networks typically work without using extracted features, although we can still use TF-IDF or Bag-of-Words features as an input. 
• Language Models : In very basic terms, the objective of a language model is to predict the next word when given a stream of input words. Probabilistic models that use Markov assumption are one example:

P(W n )=P(W n |W n−1 )

Deep learning is also used to create such language models. Deep-learning models take as input a word embedding and, at each time state, return the probability distribution of the next word as the probability for every word in the dictionary. Pre-trained language models learn the structure of a particular language by processing a large corpus, such as Wikipedia. They can then be fine-tuned for a particular task. For instance, BERT has been fine-tuned for tasks ranging from fact-checking to writing headlines . 

Top Natural Language Processing (NLP) Techniques

Most of the NLP tasks discussed above can be modeled by a dozen or so general techniques. It’s helpful to think of these techniques in two categories: Traditional machine learning methods and deep learning methods. 

Traditional Machine learning NLP techniques: 

• Logistic regression is a supervised classification algorithm that aims to predict the probability that an event will occur based on some input. In NLP, logistic regression models can be applied to solve problems such as sentiment analysis, spam detection, and toxicity classification.
• Naive Bayes is a supervised classification algorithm that finds the conditional probability distribution P(label | text) using the following Bayes formula:

P(label | text) = P(label) x P(text|label) / P(text) 

and predicts based on which joint distribution has the highest probability. The naive assumption in the Naive Bayes model is that the individual words are independent. Thus: 

P(text|label) = P(word_1|label)*P(word_2|label)*…P(word_n|label)

In NLP, such statistical methods can be applied to solve problems such as spam detection or finding bugs in software code . 

• Decision trees are a class of supervised classification models that split the dataset based on different features to maximize information gain in those splits.

• Latent Dirichlet Allocation (LDA) is used for topic modeling. LDA tries to view a document as a collection of topics and a topic as a collection of words. LDA is a statistical approach. The intuition behind it is that we can describe any topic using only a small set of words from the corpus.
• Hidden Markov models : Markov models are probabilistic models that decide the next state of a system based on the current state. For example, in NLP, we might suggest the next word based on the previous word. We can model this as a Markov model where we might find the transition probabilities of going from word1 to word2, that is, P(word1|word2). Then we can use a product of these transition probabilities to find the probability of a sentence. The hidden Markov model (HMM) is a probabilistic modeling technique that introduces a hidden state to the Markov model. A hidden state is a property of the data that isn’t directly observed. HMMs are used for part-of-speech (POS) tagging where the words of a sentence are the observed states and the POS tags are the hidden states. The HMM adds a concept called emission probability; the probability of an observation given a hidden state. In the prior example, this is the probability of a word, given its POS tag. HMMs assume that this probability can be reversed: Given a sentence, we can calculate the part-of-speech tag from each word based on both how likely a word was to have a certain part-of-speech tag and the probability that a particular part-of-speech tag follows the part-of-speech tag assigned to the previous word. In practice, this is solved using the Viterbi algorithm.

Deep learning NLP Techniques: 

• Convolutional Neural Network (CNN): The idea of using a CNN to classify text was first presented in the paper “ Convolutional Neural Networks for Sentence Classification ” by Yoon Kim. The central intuition is to see a document as an image. However, instead of pixels, the input is sentences or documents represented as a matrix of words.

• Recurrent Neural Network (RNN) : Many techniques for text classification that use deep learning process words in close proximity using n-grams or a window (CNNs). They can see “New York” as a single instance. However, they can’t capture the context provided by a particular text sequence. They don’t learn the sequential structure of the data, where every word is dependent on the previous word or a word in the previous sentence. RNNs remember previous information using hidden states and connect it to the current task. The architectures known as Gated Recurrent Unit (GRU) and long short-term memory (LSTM) are types of RNNs designed to remember information for an extended period. Moreover, the bidirectional LSTM/GRU keeps contextual information in both directions, which is helpful in text classification. RNNs have also been used to generate mathematical proofs and translate human thoughts into words. 

• Autoencoders are deep learning encoder-decoders that approximate a mapping from X to X, i.e., input=output. They first compress the input features into a lower-dimensional representation (sometimes called a latent code, latent vector, or latent representation) and learn to reconstruct the input. The representation vector can be used as input to a separate model, so this technique can be used for dimensionality reduction. Among specialists in many other fields, geneticists have applied autoencoders to spot mutations associated with diseases in amino acid sequences. 

• Encoder-decoder sequence-to-sequence : The encoder-decoder seq2seq architecture is an adaptation to autoencoders specialized for translation, summarization, and similar tasks. The encoder encapsulates the information in a text into an encoded vector. Unlike an autoencoder, instead of reconstructing the input from the encoded vector, the decoder’s task is to generate a different desired output, like a translation or summary. 

• Transformers : The transformer, a model architecture first described in the 2017 paper “ Attention Is All You Need ” (Vaswani, Shazeer, Parmar, et al.), forgoes recurrence and instead relies entirely on a self-attention mechanism to draw global dependencies between input and output. Since this mechanism processes all words at once (instead of one at a time) that decreases training speed and inference cost compared to RNNs, especially since it is parallelizable. The transformer architecture has revolutionized NLP in recent years, leading to models including BLOOM , Jurassic-X , and Turing-NLG . It has also been successfully applied to a variety of different vision tasks , including making 3D images .

Six Important Natural Language Processing (NLP) Models

Over the years, many NLP models have made waves within the AI community, and some have even made headlines in the mainstream news. The most famous of these have been chatbots and language models. Here are some of them:

• Eliza was developed in the mid-1960s to try to solve the Turing Test; that is, to fool people into thinking they’re conversing with another human being rather than a machine. Eliza used pattern matching and a series of rules without encoding the context of the language.
• Tay was a chatbot that Microsoft launched in 2016. It was supposed to tweet like a teen and learn from conversations with real users on Twitter. The bot adopted phrases from users who tweeted sexist and racist comments, and Microsoft deactivated it not long afterward. Tay illustrates some points made by the “Stochastic Parrots” paper, particularly the danger of not debiasing data.
• BERT and his Muppet friends: Many deep learning models for NLP are named after Muppet characters , including ELMo , BERT , Big BIRD , ERNIE , Kermit , Grover , RoBERTa , and Rosita . Most of these models are good at providing contextual embeddings and enhanced knowledge representation.
• Generative Pre-Trained Transformer 3 (GPT-3) is a 175 billion parameter model that can write original prose with human-equivalent fluency in response to an input prompt. The model is based on the transformer architecture. The previous version, GPT-2, is open source. Microsoft acquired an exclusive license to access GPT-3’s underlying model from its developer OpenAI, but other users can interact with it via an application programming interface (API). Several groups including EleutherAI and Meta have released open source interpretations of GPT-3. 
• Language Model for Dialogue Applications (LaMDA) is a conversational chatbot developed by Google. LaMDA is a transformer-based model trained on dialogue rather than the usual web text. The system aims to provide sensible and specific responses to conversations. Google developer Blake Lemoine came to believe that LaMDA is sentient. Lemoine had detailed conversations with AI about his rights and personhood. During one of these conversations, the AI changed Lemoine’s mind about Isaac Asimov’s third law of robotics. Lemoine claimed that LaMDA was sentient, but the idea was disputed by many observers and commentators. Subsequently, Google placed Lemoine on administrative leave for distributing proprietary information and ultimately fired him.
• Mixture of Experts ( MoE): While most deep learning models use the same set of parameters to process every input, MoE models aim to provide different parameters for different inputs based on efficient routing algorithms to achieve higher performance . Switch Transformer is an example of the MoE approach that aims to reduce communication and computational costs.

Programming Languages, Libraries, And Frameworks For Natural Language Processing (NLP)

Many languages and libraries support NLP. Here are a few of the most useful.

• Natural Language Toolkit (NLTK) is one of the first NLP libraries written in Python. It provides easy-to-use interfaces to corpora and lexical resources such as WordNet . It also provides a suite of text-processing libraries for classification, tagging, stemming, parsing, and semantic reasoning.
• spaCy is one of the most versatile open source NLP libraries. It supports more than 66 languages. spaCy also provides pre-trained word vectors and implements many popular models like BERT. spaCy can be used for building production-ready systems for named entity recognition, part-of-speech tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, entity linking, and so on.
• Deep Learning libraries: Popular deep learning libraries include TensorFlow and PyTorch , which make it easier to create models with features like automatic differentiation. These libraries are the most common tools for developing NLP models.
• Hugging Face offers open-source implementations and weights of over 135 state-of-the-art models. The repository enables easy customization and training of the models.
• Gensim provides vector space modeling and topic modeling algorithms.
• R : Many early NLP models were written in R, and R is still widely used by data scientists and statisticians. Libraries in R for NLP include TidyText , Weka , Word2Vec , SpaCyR , TensorFlow , and PyTorch .
• Many other languages including JavaScript, Java, and Julia have libraries that implement NLP methods.

Controversies Surrounding Natural Language Processing (NLP)

NLP has been at the center of a number of controversies. Some are centered directly on the models and their outputs, others on second-order concerns, such as who has access to these systems, and how training them impacts the natural world. 

• Stochastic parrots: A 2021 paper titled “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” by Emily Bender, Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell examines how language models may repeat and amplify biases found in their training data. The authors point out that huge, uncurated datasets scraped from the web are bound to include social biases and other undesirable information, and models that are trained on them will absorb these flaws. They advocate greater care in curating and documenting datasets, evaluating a model’s potential impact prior to development, and encouraging research in directions other than designing ever-larger architectures to ingest ever-larger datasets.
• Coherence versus sentience: Recently, a Google engineer tasked with evaluating the LaMDA language model was so impressed by the quality of its chat output that he believed it to be sentient . The fallacy of attributing human-like intelligence to AI dates back to some of the earliest NLP experiments. 
• Environmental impact: Large language models require a lot of energy during both training and inference. One study estimated that training a single large language model can emit five times as much carbon dioxide as a single automobile over its operational lifespan. Another study found that models consume even more energy during inference than training. As for solutions, researchers have proposed using cloud servers located in countries with lots of renewable energy as one way to offset this impact. 
• High cost leaves out non-corporate researchers: The computational requirements needed to train or deploy large language models are too expensive for many small companies . Some experts worry that this could block many capable engineers from contributing to innovation in AI. 
• Black box: When a deep learning model renders an output, it’s difficult or impossible to know why it generated that particular result. While traditional models like logistic regression enable engineers to examine the impact on the output of individual features, neural network methods in natural language processing are essentially black boxes. Such systems are said to be “not explainable,” since we can’t explain how they arrived at their output. An effective approach to achieve explainability is especially important in areas like banking, where regulators want to confirm that a natural language processing system doesn’t discriminate against some groups of people, and law enforcement, where models trained on historical data may perpetuate historical biases against certain groups.

“ Nonsense on stilts ”: Writer Gary Marcus has criticized deep learning-based NLP for generating sophisticated language that misleads users to believe that natural language algorithms understand what they are saying and mistakenly assume they are capable of more sophisticated reasoning than is currently possible.

How To Get Started In Natural Language Processing (NLP)

If you are just starting out, many excellent courses can help.

If you want to learn more about NLP, try reading research papers. Work through the papers that introduced the models and techniques described in this article. Most are easy to find on arxiv.org . You might also take a look at these resources: 

• The Batch : A weekly newsletter that tells you what matters in AI. It’s the best way to keep up with developments in deep learning.
• NLP News : A newsletter from Sebastian Ruder, a research scientist at Google, focused on what’s new in NLP. 
• Papers with Code : A web repository of machine learning research, tasks, benchmarks, and datasets.

We highly recommend learning to implement basic algorithms (linear and logistic regression, Naive Bayes, decision trees, and vanilla neural networks) in Python. The next step is to take an open-source implementation and adapt it to a new dataset or task. 

NLP is one of the fast-growing research domains in AI, with applications that involve tasks including translation, summarization, text generation, and sentiment analysis. Businesses use NLP to power a growing number of applications, both internal — like detecting insurance fraud , determining customer sentiment, and optimizing aircraft maintenance — and customer-facing, like Google Translate. 

Aspiring NLP practitioners can begin by familiarizing themselves with foundational AI skills: performing basic mathematics, coding in Python, and using algorithms like decision trees, Naive Bayes, and logistic regression. Online courses can help you build your foundation. They can also help as you proceed into specialized topics. Specializing in NLP requires a working knowledge of things like neural networks, frameworks like PyTorch and TensorFlow, and various data preprocessing techniques. The transformer architecture, which has revolutionized the field since it was introduced in 2017, is an especially important architecture.

NLP is an exciting and rewarding discipline, and has potential to profoundly impact the world in many positive ways. Unfortunately, NLP is also the focus of several controversies, and understanding them is also part of being a responsible practitioner. For instance, researchers have found that models will parrot biased language found in their training data, whether they’re counterfactual, racist, or hateful. Moreover, sophisticated language models can be used to generate disinformation. A broader concern is that training large models produces substantial greenhouse gas emissions.

This page is only a brief overview of what NLP is all about. If you have an appetite for more, DeepLearning.AI offers courses for everyone in their NLP journey, from AI beginners and those who are ready to specialize . No matter your current level of expertise or aspirations, remember to keep learning!

NLP Applications in Voice Recognition

NLP and Voice Recognition are complementary but different. Voice Recognition focuses on processing voice data to convert it into a structured form such as text. NLP focuses on understanding the meaning by processing text input. Voice Recognition can work without NLP , but NLP cannot directly process audio inputs. Yet, without NLP , Voice Recognition cannot understand what humans mean. That’s why we see them used together. Let’s look at some NLP applications in voice control , speech analytics , and governance and compliance use cases.

NLP in Voice Command and Control

Voice Assistants is one of the most known NLP applications in voice command and control. Amazon's Alexa and Alphabet’s Google Assistant use Voice Recognition to process voice commands and NLP to understand and respond if needed.

In the example below, Speech-to-Text (subtopic of Voice Recognition ) transcribes the command “Set an alarm for 7:30 in the morning” and returns the text output. Natural Language Understanding (subtopic of NLP ) processes the text, extracts the meaning, and triggers an action to set the alarm at 7 am. Using Speech-to-Text and Natural Language Understanding together is also known as Spoken Language Understanding . Siri’s response: “OK, I set an alarm for 7:30 AM.” is powered by Natural Language Generation (subtopic of NLP ).

NLP in Speech Analytics

Social Listening or Social Media Listening is not new for many. Most enterprises monitor posts and comments on Twitter, Instagram, Yelp, or Foursquare. However, now social media users “talk” more than they “write”. Platforms such as TikTok, Snapchat, or Twitch are more popular , especially among younger generations. Voice Recognition and NLP jointly add “listening” and “understanding” to simple social media monitoring. Enterprises broaden their coverage on social media by using Voice Recognition and NLP together.

Voice Recognition is not just limited to Speech-to-Text . Using Speech Emotion Recognition (subtopic of Voice Recognition ) and Sentiment Analysis (subtopic of NLP ) jointly enables enterprises to understand speakers’ semantic and vocal emotions.

NLP in Governance and Compliance

Voice Chat Monitoring and Moderation has been used mainly by call centers to comply with regulations and train agents. They randomly select sample interactions to audit, which wouldn’t capture more than two percent . Advances in Voice Recognition have increased this ratio by achieving higher accuracy and lower costs . Enterprises started transcribing and processing more interactions. Now they select interactions based on keywords and sentiments rather than randomly.

Voice Chat Monitoring and Moderation is not limited to conversations between users and service providers. Conversations among users in multiplayer games require moderation. Online harassment affects player experience significantly. ADL ’s survey shows that in the past six months, 83% of adults aged 18-45, representing 80M and 60% of young people aged 13-17, representing 14M experienced online harassment in multiplayer games. Not surprisingly, all major gaming platforms such as Stream highlight the importance of moderation, or Roblox publish community standards. Unity recently acquired a company to achieve safer gaming environments.

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Five years ago, in his 2018 Congress testimony, Mark Zuckerberg said AI would take a primary role in automatically detecting hate speech on ...

Using Speech to Text in voice assistants is the common approach. The Conventional Spoken Language Understanding method transcribes speech da...

Natural Language Understanding (NLU) is a subtopic of Natural Language Processing.

Natural Language Processing (NLP) focuses on 'understanding' a given content by extracting information from it.

Estimations of the current Natural Language Processing (NLP) market size varies between $11B and $16B.

Spoken Language Understanding (SLU) sits at the intersection of speech recognition and natural language processing.

Choosing the best Natural Language Understanding (NLU) software is difficult.

Learn the differences between end-to-end and hybrid speech-to-text systems in terms of accuracy, features, and total cost of ownership.

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Join the community, add a new evaluation result row, text-to-speech synthesis.

93 papers with code • 6 benchmarks • 17 datasets

Text-To-Speech Synthesis is a machine learning task that involves converting written text into spoken words. The goal is to generate synthetic speech that sounds natural and resembles human speech as closely as possible.

Benchmarks Add a Result

Most implemented papers

Fastspeech 2: fast and high-quality end-to-end text to speech.

In this paper, we propose FastSpeech 2, which addresses the issues in FastSpeech and better solves the one-to-many mapping problem in TTS by 1) directly training the model with ground-truth target instead of the simplified output from teacher, and 2) introducing more variation information of speech (e. g., pitch, energy and more accurate duration) as conditional inputs.

Tacotron: Towards End-to-End Speech Synthesis

A text-to-speech synthesis system typically consists of multiple stages, such as a text analysis frontend, an acoustic model and an audio synthesis module.

Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention

This paper describes a novel text-to-speech (TTS) technique based on deep convolutional neural networks (CNN), without use of any recurrent units.

FastSpeech: Fast, Robust and Controllable Text to Speech

In this work, we propose a novel feed-forward network based on Transformer to generate mel-spectrogram in parallel for TTS.

Efficient Neural Audio Synthesis

The small number of weights in a Sparse WaveRNN makes it possible to sample high-fidelity audio on a mobile CPU in real time.

Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram

We propose Parallel WaveGAN, a distillation-free, fast, and small-footprint waveform generation method using a generative adversarial network.

Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis

In this work, we propose "global style tokens" (GSTs), a bank of embeddings that are jointly trained within Tacotron, a state-of-the-art end-to-end speech synthesis system.

Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis

Clone a voice in 5 seconds to generate arbitrary speech in real-time

FastSpeech: Fast,Robustand Controllable Text-to-Speech

Compared with traditional concatenative and statistical parametric approaches, neural network based end-to-end models suffer from slow inference speed, and the synthesized speech is usually not robust (i. e., some words are skipped or repeated) and lack of controllability (voice speed or prosody control).

WaveGrad: Estimating Gradients for Waveform Generation

This paper introduces WaveGrad, a conditional model for waveform generation which estimates gradients of the data density.

While natural language processing (NLP), natural language understanding (NLU), and natural language generation (NLG) are all related topics, they are distinct ones. At a high level, NLU and NLG are just components of NLP. Given how they intersect, they are commonly confused within conversation, but in this post, we’ll define each term individually and summarize their differences to clarify any ambiguities.

What is natural language processing?

Natural language processing , which evolved from computational linguistics, uses methods from various disciplines, such as computer science, artificial intelligence, linguistics, and data science, to enable computers to understand human language in both written and verbal forms. While computational linguistics has more of a focus on aspects of language, natural language processing emphasizes its use of machine learning and deep learning techniques to complete tasks, like language translation or question answering. Natural language processing works by taking unstructured data and converting it into a structured data format. It does this through the identification of named entities (a process called named entity recognition) and identification of word patterns, using methods like tokenization, stemming, and lemmatization, which examine the root forms of words. For example, the suffix -ed on a word, like called, indicates past tense, but it has the same base infinitive (to call) as the present tense verb calling.

While a number of NLP algorithms exist, different approaches tend to be used for different types of language tasks. For example, hidden Markov chains tend to be used for part-of-speech tagging. Recurrent neural networks help to generate the appropriate sequence of text. N-grams, a simple language model (LM), assign probabilities to sentences or phrases to predict the accuracy of a response. These techniques work together to support popular technology such as chatbots, or speech recognition products like Amazon’s Alexa or Apple’s Siri. However, its application has been broader than that, affecting other industries such as education and healthcare.

What is natural language understanding?

Natural language understanding is a subset of natural language processing, which uses syntactic and semantic analysis of text and speech to determine the meaning of a sentence. Syntax refers to the grammatical structure of a sentence, while semantics alludes to its intended meaning. NLU also establishes a relevant ontology: a data structure which specifies the relationships between words and phrases. While humans naturally do this in conversation, the combination of these analyses is required for a machine to understand the intended meaning of different texts. Our ability to distinguish between homonyms and homophones illustrates the nuances of language well. For example, let’s take the following two sentences:

Learn more about IBM Watson Natural Language Understanding.

• Alice is swimming against the current.
• The current version of the report is in the folder.

In the first sentence, the word, current is a noun. The verb that precedes it, swimming, provides additional context to the reader, allowing us to conclude that we are referring to the flow of water in the ocean. The second sentence uses the word current, but as an adjective. The noun it describes, version, denotes multiple iterations of a report, enabling us to determine that we are referring to the most up-to-date status of a file.

These approaches are also commonly used in data mining to understand consumer attitudes. In particular, sentiment analysis enables brands to monitor their customer feedback more closely, allowing them to cluster positive and negative social media comments and track net promoter scores. By reviewing comments with negative sentiment, companies are able to identify and address potential problem areas within their products or services more quickly.

What is natural language generation?

Natural language generation is another subset of natural language processing. While natural language understanding focuses on computer reading comprehension, natural language generation enables computers to write. NLG is the process of producing a human language text response based on some data input. This text can also be converted into a speech format through text-to-speech services.

NLG also encompasses text summarization capabilities that generate summaries from in-put documents while maintaining the integrity of the information. Extractive summarization is the AI innovation powering Key Point Analysis used in That’s Debatable.

Initially, NLG systems used templates to generate text. Based on some data or query, an NLG system would fill in the blank, like a game of Mad Libs. But over time, natural language generation systems have evolved with the application of hidden Markov chains, recurrent neural networks, and transformers, enabling more dynamic text generation in real time.

Learn more about IBM Watson Discovery.

As with NLU, NLG applications need to consider language rules based on morphology, lexicons, syntax and semantics to make choices on how to phrase responses appropriately. They tackle this in three stages:

• Text planning: During this stage, general content is formulated and ordered in a logical manner.
• Sentence planning: This stage considers punctuation and text flow, breaking out content into paragraphs and sentences and incorporating pronouns or conjunctions where appropriate.
• Realization: This stage accounts for grammatical accuracy, ensuring that rules around punctation and conjugations are followed. For example, the past tense of the verb run is ran , not runned .

NLP vs NLU vs. NLG summary

• Natural language processing (NLP) seeks to convert unstructured language data into a structured data format to enable machines to understand speech and text and formulate relevant, contextual responses. Its subtopics include natural language processing and natural language generation.
• Natural language understanding (NLU) focuses on machine reading comprehension through grammar and context, enabling it to determine the intended meaning of a sentence.
• Natural language generation (NLG) focuses on text generation, or the construction of text in English or other languages, by a machine and based on a given dataset.

Infuse your data for AI

Natural language processing and its subsets have numerous practical applications within today’s world, like healthcare diagnoses or online customer service.

Explore some of the latest NLP research at IBM or take a look at some of IBM’s product offerings, like Watson Natural Language Understanding . Its text analytics service offers insight into categories, concepts, entities, keywords, relationships, sentiment, and syntax from your textual data to help you respond to user needs quickly and efficiently. Help your business get on the right track to analyze and infuse your data at scale for AI.

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The evolution of human society was made possible by language and communication, so it’s reasonable for us to want the same level of advancement for computers. However, we struggle with the massive amounts of language data we encounter daily. If computers could handle large-scale text and voice data with precision, they could revolutionize our lives. Natural Language Processing (NLP) has led to many innovations like Alexa and Siri.

Training a machine model to understand human languages is challenging due to the complexity of languages. In addition, countless nuances, dialects, and regional variations take much work to standardize. The latest breakthrough in natural language processing is Text-to-Speech (TTS) – a form of NLP that can convert written data into audio files with excellent speech quality. This blog post will examine how text-to-speech revolutionizes natural language processing and its applications.

What is natural language processing?

Natural language processing is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence that deals with the ability of computers to understand, interpret, and generate human language. It analyzes large amounts of natural language data to understand how humans communicate. Natural language processing has existed since the early 1990s , but it has become increasingly important as technology advances and more data becomes available.

Natural language processing allows computers to interpret and manipulate human language, making it possible to understand what people are saying or writing and respond accordingly. NLP has become increasingly important due to its potential applications in various fields, like healthcare, finance, and education. In addition, it can be used for AI tools and to automate tasks like chatbots, voice generators , and more.

How does natural language processing work?

NLP analyzes the structure and meaning of natural language to extract useful information from it. NLP also uses syntax to assess and determine the significance of a language based on grammatical rules. Parsing is a syntax technique that involves analyzing the grammar of a sentence.

Using syntax techniques involves breaking down the text into smaller components, such as words or phrases, and then using algorithms to identify patterns in the data. Once these patterns are identified, they can be used to generate output, such as a text-to-speech model or lifelike voices.

What is text-to-speech technology?

Text-to-Speech technology is a type of speech synthesis that transforms written text into spoken words using computer algorithms. It enables machines to communicate with humans in a natural-sounding voice by processing text into synthesized speech. TTS systems typically use a combination of linguistic rules and statistical models to generate synthetic speech.

What is speech synthesis?

Speech synthesis refers to the process of using a computer to produce artificial human speech. It’s a generative model commonly used to convert written text into audio information and is utilized in voice-enabled services and mobile applications.

How do TTS tools work?

Natural language processing helps address these challenges by providing tools for understanding how humans communicate through their choice of words and phrases when speaking or writing. TTS systems can then use this understanding to generate more accurate synthetic speech reflecting the input text’s intended meaning. As a result, TTS technology has become increasingly important in modern communication as it allows machines to interact with humans more effectively than ever before.

Applications of natural language processing

NLP can be applied in various fields, such as sentiment analysis, chatbots, language translation, etc. Here are some examples:

• Sentiment Analysis : This type uses algorithms to analyze text data for sentiment or opinion expressed by the author. Businesses can use this to gain insights into customers’ views about their products or services.
• Voice Assistants and Chatbots : These computer programs use natural language processing technology to respond to commands by users. They can be used to play music, set reminders, or answer questions about products or services. Chatbots are similar but interact with users through text messages instead of voice commands.
• Email Filtering : This involves sorting emails according to specific criteria, such as sender address or subject line, using natural language processing algorithms. This can help reduce spam emails and make it easier for users to find relevant emails quickly without manually sorting them all individually.
• Language Translation : this application enables computers to automatically translate text from one language into another using algorithms trained on large datasets of translated sentences from different languages. This can help people communicate with each other across languages without having to learn multiple languages firsthand.

Is speech synthesis related to NLP?

There’s nothing like a good old conversation about speech synthesis and NLP. But to answer the burning question, yes, speech synthesis is indeed related to NLP. Speech synthesis, also a subfield of NLP, deals with converting text into spoken language. 

Without NLP, speech synthesis would be nothing more than a robot monotone voice reciting words on a page. So, next time you listen to Siri, Alexa, or hear any other virtual assistant speak, you can thank NLP for enabling that human-like tone to be achieved. 

Can NLP help create synthetic voices for content creation?

Natural language processing can create synthetic voices for content creation. NLP can generate speech almost indistinguishable from authentic human voices using sophisticated algorithms and models. This technology is becoming increasingly popular, allowing businesses to save time and money instead of hiring voice actors or recording real-life audio.

Furthermore, NLP enables personalized speech customized to the user’s preferences. This can help create a more immersive, personal, and engaging customer experience when interacting with digital content.

How does NLP apply to text-to-speech technology?

The text-to-speech technology utilizes algorithms that process natural language and speech synthesis to automatically convert written text into spoken words without a human intervening. Using NLP technologies and TTS tools together allows people with difficulty reading due to physical disabilities to access written material without having trouble understanding it. In addition, this technology provides easy access to educational materials for people facing financial constraints who need help to purchase books.

NLP techniques help TTS tools understand written words and convert them into natural-sounding speech. With an advanced NLP framework for high-quality TTS synthesis systems, developers can create more realistic synthesized speech. However, two essential components are needed to make this system function properly: a stage for natural language processing and speech synthesis.

Does an AI voice cloner use NLP?

Yes, an AI voice cloner does use NLP. Voice cloning is a technology that uses AI and TTS technology to clone a recorded human voice. It mimics a speaker’s intonation, pronunciation, and other characteristics to create a clone or a virtual copy of the original voice.

To achieve this, the AI voice cloner must first analyze and record the audio input using an NLP algorithm. This allows it to extract information about other vocal characteristics of the speaker. This information is then used to create a virtual clone or a replica of the original voice. By combining AI and NLP, this technology can create realistic synthetic voices that sound just like the natural person.

Voice cloning is another powerful tool for content creators, allowing them to easily create voices for their digital content without hiring voice actors or recording audio.

Can NLP be used to create a deepfake voice?

Yes, NLP can be used to create a deepfake voice. Deepfakes are AI-generated audio clips that mimic the sound of a natural person’s voice. They can generate realistic-sounding audio clips of the target voice that can easily be mistaken for an authentic voice using natural language processing, audio synthesis, and AI algorithms.

An excellent example is the Barrack Obama voice generator, which uses NLP and AI algorithms to generate a voice resembling that of the former US president. People often use cloned voices to have fun, create original content, or play pranks on their loved ones. Specific AI software lets you use the cloned voice as-is or modify it with tone, intonation, and rhythm variations to produce a slightly different custom voice-over.

Although there is no legislation regarding the voice cloning of famous people and other public figures, creators should still be careful and ensure they work with files not protected by copyright.

Pros And Cons Of TTS

Everything technology has pros and cons, and TTS technology is no different. However, there are many advantages of TTS technology, including:

• Its ability to save time by automating tasks that would typically require manual labor.
• Your content can reach visually impaired people.
• Using TTS tools is cost-effective compared to hiring professional voice actors.
• TTS tools are more flexible when creating different types of voices for other purposes.

Most TTS tools have a library of male and female voices or can emulate different accents and languages. However, traditional challenges in TTS include generating natural-sounding voices that accurately reflect the intended meaning of the input text. There are also some issues, such as awkward generations when speaking, which can make conversations seem robotic, and difficulty understanding complex sentences, context, emotions, etc.

There are limitations to NLP systems in TTS tools. Computers can have a hard time understanding the context of natural language data. They may need help interpreting slang words or idioms. Moreover, they might not be able to identify when someone is being sarcastic or ironic.

How to create memorable audio files for your content needs

Text-to-speech technology has come a long way over the years, thanks partly to advances in natural language processing algorithms that allow computers to understand human language inputs better. These advancements bring new opportunities for businesses and consumers who want access to powerful, easy-to-use communication tools. At Typecast, we create and harness the power of NLP and TTS systems to enable our customers to quickly create memorable audio files for various content needs.

Our platform offers a wide range of features allowing you to create engaging audio files from scratch or use existing text. In addition, you can customize how your audio file sounds by selecting different voices, accents, and languages. If you want to make fun and exciting content, you could also use our new Joe Biden voice generator to create audio recordings or clips that sound like the current US president.

Use your own voice with NLP and text-to-speech with Typecast

Text-to-speech technology has come a long way and is now essential to content creation. With the help of natural language processing algorithms, Typecast makes it easy for businesses and individuals to create engaging audio files from scratch or by using existing text.

If you’re not into creating memes or using celebrity impersonations, text-to-speech technology can also create audio files that feature your unique voice. Our text-to-speech system makes creating audio files that sound just like you easy. We use natural language processing and machine and deep learning algorithms to understand your voice and generate audio files that accurately represent it.

Our platform offers various customization options for voiceovers, allowing you to create unique audio files with just the right tone. You can adjust the speed, intonation, pitch, and more to make your audio files sound exactly like you. In addition, with our platform, you can create custom voices and accents to boost your channel’s traffic and stand out from other creators.

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IMO Clinical AI: A modern NLP development toolset

• Published on May 21, 2024
• By  Rajiv Haravu

In the first blog of this series , we covered the building blocks of IMO Clinical AI – terminology, technology, and people. The focus of this article is technology, especially the tools that aid in the construction and deployment of natural language processing (NLP) pipelines , a series of inter-connected steps that help convert text into a desired output for downstream analysis . 1

The NLP development platform in IMO Clinical AI consists of an integrated development environment that manages the entire NLP pipeline development lifecycle.

Four steps to develop NLP pipelines/models

1. data acquisition:.

NLP , a sub-discipline of artificial intelligence (AI), is fundamentally about learning from free text examples and extracting meaning from such text by recognizing entities and relationships within free text narratives. Data acquisition is about obtaining textual data from various sources to aid in the creation of NLP pipelines. IMO has secure technical means and appropriate policies to acquire the requisite data.

2. Converting images and PDFs to text:

Free-text narratives in healthcare data reside largely in formats such as PDFs and images. The NLP development toolset uses various optical character recognition (OCR) methods to convert images and PDFs to text.

3. Integrated Development Environment (IDE) for model training and NLP pipeline construction:

The NLP development platform in IMO Clinical AI also consists of an IDE to provide the user with an intuitive user interface (UI) for the construction of NLP pipelines. The functions of the IDE can broadly be grouped into the following categories:

• Text pre-processing: The IDE provides easy access to pre-processing techniques like tokenization, lemmatization, part-of-speech (POS) tagging, and much more.
• Feature engineering: The IDE also provides ways to extract relevant features from raw text to make them available in a form that is conducive for training machine learning (ML) models.
• Model training: Once the features are prepared, the IDE offers various deep learning algorithms and frameworks to train models on the processed data. This step involves selecting appropriate algorithms and tuning hyperparameters to ensure it successfully trains a high perfoming model on the training data.
• NLP pipeline construction and evaluation: After pre-processing and feature engineering steps are complete, the IDE provides an easy to use and intuitive user interface that helps the user combine ML approaches, heuristic approaches, calls to external APIs, and much more to help finish the construction of a functioning NLP pipeline. The IDE also has tools to evaluate and measure the pipeline’s performance.

4. Deployment:

After the NLP pipeline has been constructed and evaluated for its performance, the NLP development toolset provides a variety of methods to deploy the pipeline so that it can be easily accessible to third-party applications.

Development of NLP pipelines needs to be understood as having a lifecycle of its own. IMO Clinical AI endeavors to provide NLP architects, data analysts, and clinical subject matter experts an easy to use and integrated development environment that eases the journey through the lifecycle – helping customers develop and deploy NLP pipelines in service of their business objectives.

Click here to learn more about IMO Clinical AI and here to learn how our AI-powered solutions simplify clinical workflows and boost healthcare data quality.

1 https://medium.com/@asjad_ali/understanding-the-nlp-pipeline-a-comprehensiveguide-828b2b3cd4e2#:~:text=In%20Natural%20Language%20Processing%20(NLP,it%20reaches% 20its%20final%20form

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Introduction To Natural Language Processing (NLP) Technology

Learn how natural language processing shapes our digital interactions, making technology more intuitive. click to explore the breakthroughs and future of nlp.

NLP (Natural Language Processing) is a cutting-edge AI method that helps computers understand and respond to human language.

NLP underpins everything from simple requests to com plicated systems like smartphones and stock markets that financial sectors can examine.

This paper details how NLP works, its history, and its widespread use, including how it may be used to find the optimal FX strategy.

This is a brief look at how it is changing the digital world and making difficult activities like currency trading more enticing.

What is Natural Language Processing?

Natural Language Processing (NLP) is a personification of the contemporary discipline that successfully combines computer science, AI, and linguistic knowledge. It empowers computers to comprehend, decode, and create language, as well as make communication more effective. NLP is the technology behind voice-controlled GPS systems, artificial intelligence, and machine learning, in which digital assistants are included, as well as human agents’ reproduction for automated customer service applications. It, as it is, intends to give the impression of the harmony of humans and machines so that all users can experience the technology in a more user-friendly and simple way.

How NLP Works

Natural Language Processing (NLP) is a complicated study that uses algorithms that analyze the language people use spontaneously, turning incoherent, equivocal data into data processed by machines. This intricate process spans multiple stages: Because it has several components, some of these are lexical analysis of syntax, semantic analysis, which looks at context, and pragmatic analysis, which considers the use of language. The financial sector may cope most with these competencies. For instance, NLP can analyze vast amounts of financial news and expert commentary to identify the best forex strategy, interpreting not just the words but the subtleties of language—like slang or regional expressions—humans use daily. This way, NLP helps computers understand the complexities and nuances of human language, including in specialized fields like forex trading.

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Historical background of nlp.

The story of Natural Language Processing (NLP) traces back to the 1950s, principally with the objective of developing computers that could translate between languages. The initial concept was machine learning rule-based methods, in which linguists applied their manual language rules to computers.

During that time, the first machine translation projects were born, which gave computers the opportunity to perceive naturally spoken human languages and translate them. Core Techniques and Models in NLP

Text analysis in NLP is an important step toward text understanding. It consists of the text processing and analysis phase.

For instance, the process covers detecting the smallest words or components to enable analysis (tokenization), choosing the part of speech of each word (POS tagging), and recognizing the sentence diagram structure (parsing).

Language modeling, the other fundamental, implies the building of mathematical models that demonstrate the ability to comprehend language, a concept. Such models can predict the probability of a sequence of words being shown in a sentence, which is one of the most important tasks like text completion or corrections.

Machine Learning in NLP

One of the major driving factors that has brought NLP to the forefront is the emergence of powerful machine learning and deep learning. Using astronomical text data for training these models enables them to do these and other kinds of tasks, such as translation, question-answering, and opinion analysis, without even the need for programming with explicit rules.

This method has practically brought a larger-scale application of NLP processes whose accuracy and efficacy have remarkably improved, making them more foolproof and complex.

Applications and Challenges

The advent of NLP is disrupting the patterns of human users as technologies are adapting to new interaction methods.

It’s through it our voice assistants like Siri and Alexa process and act upon them-extracting commands from your speech and converting them into spoken words. In customer service daily, chatbots utilize NLP algorithms to resolve queries and provide support around the clock.

Further applications include sentiment analysis, which determines the forced impressions towards text on Facebook, for example, and machine translation services like Google Translate, which do not let you down when there is a language barrier.

Overcoming Challenges

Even though NLP is outstanding in its diversity, it is not without drawbacks. While feeling the context and irony means that the sentence can change its meaning widely – this is one big obstacle to be overcome.

Furthermore, language nature is ambiguous, and a word has many forms of meaning, which makes it challenging for machines to catch correctly what is said in human language communication.

Another point is that dealing with the immutability of language is a continuous process, which means that the emergence of new slang and phrases calls for the model’s constant revision and update. Looking Ahead: The Future of NLP

The future of NLP is promising, as more research centers on eliminating all existing limitations. There are ongoing attempts to master the ability to adapt to and deal with the complexities of humans’ native tongues.

In sophisticated deep learning, the neural network installation will be the apex for NLP systems to comprehend and process language at a higher level, which, therefore, will impact human interaction with machines.

The Impact on Society

With the development in the domain of NLP, its influence on society is going to be felt at even higher levels.

Such a chatbot will smoothen out the process of integration of technology into daily life, making all the dialogs between a computer and a person faster and seeming human-like.

This may serve as the start of an important turn in how people will learn, get information, and communicate with each other and machines.

All possibilities are open, starting with customized learning or better functioning and universal health care that are now within reach.

Natural language processing undeniably comes as a contender to situate itself at the crossover of the capacity of human language and computer-controlled data handling.

While this domain certainly encounters quite a few issues, these issues are not actually an obstacle to the revolutionary technological revolution, in my point of view.

Through persistent progress and science, NLP is going to refine human-computer communication even further, bringing new interaction toolkits as well as novel ways of accessibility.

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How to use Google Search without AI: the ‘udm=14’ work around

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It’s hard to conceive given how popular and entrenched in modern society it is, but the Google Search of today is very different than the same product even just a few years back.

The most obvious change in recent times has been the addition of generative AI search results, also known as “ AI Overview .” Formerly an experimental option called “ Search Generative Experience ” that users had to elect through Google Labs, the addition of these results — generated from whole cloth every time you search using Google’s Gemini AI models — seek to summarize and pull out the most relevant and important information based on your search query.

Google is making this the default search experience now in the U.S. (and soon, around the world) following its I/O conference last week , a bid to compete and offset the rise of competitors such as Perplexity and OpenAI’s ChatGPT.

Yet many users have openly complained about the new Google Gen AI search results, noting that they are frequently inaccurate — even dangerously so, at times.

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Good ol’ Google AI: telling you to do the exact things you *are not supposed to do* when bitten by a rattlesnake. From mushrooms to snakebites, AI content is genuinely dangerous. pic.twitter.com/UZXgBjsre9 — ern. (@ErinEARoss) May 19, 2024

Fortunately, there is a solution for those users seeking to return to a more “pure” and pristine Google Search experience unmarred by Gen AI results.

Google added a new “Web” tab to its search engine at the top that strips away all the Gen AI results and even its older “ Featured Snippets ,” which ripped text out of web pages and reproduced them at the top of the search engine results page (SERP). It also seems to remove most ads/sponsored posts.

However, there is no way to get this option to stay as the default on Google, at least not officially. You have to search, see the AI results, and then tab over every time.

While navigating to this tab every time you want to search can be cumbersome, my old colleague Ernie Smith of the blog Tedium has found a clever work around that some users are cheering.

As he writes :

“ …Is there anything you can do to minimize the pain of having to click the “Web” option buried in a menu every single time?

The answer to that question is yes. Google does not make it easy, because its URLs seem extra-loaded with cruft these days, but by adding a URL parameter to your search—in this case, “udm=14”—you can get directly to the Web results in a search. “

In fact, as long as you set your default search engine in your browser or bookmark the following URL: “ https://www.google.com/search?q=%s&udm=14 ” you should be able to get the Web, Gen AI-free version of Google every time you search.

On X, users loved Smith’s discovery and are eagerly embracing it:

It works lol New search After adding udm=14 https://t.co/67IGiXN8Hv pic.twitter.com/X9sgw2HIUW — Lauren McKenzie (@TheMcKenziest) May 20, 2024

It will be fascinating to see how wide this work around spread. If enough people choose to go this route, will Google reconsider making Gen AI summarized search results the new default, and switch back to this more uncluttered and “purer” version of Search — a list of “blue links”?

Time will tell.

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