Spech Recognition

What is speech recognition?

Speech recognition, also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, is a capability which enables a program to process human speech into a written format. While it’s commonly confused with voice recognition, speech recognition focuses on the translation of speech from a verbal format to a text one whereas voice recognition just seeks to identify an individual user’s voice.

Key features of effective speech recognition

There are numerous speech recognition applications and devices available, but the most advanced solutions employ AI and machine learning. To understand and process human speech, they integrate grammar, syntax, structure, and composition of audio and voice signals. They should ideally learn as they go, evolving responses with each interaction.

The best systems also enable organizations to customize and adapt the technology to their specific needs, including everything from language and speech nuances to brand recognition. As an example:

• Language weighting:?Improve precision by weighting specific words that are spoken frequently (such as product names or industry jargon), beyond terms already in the base vocabulary.
• Speaker labeling:?Output a transcription that cites or tags each speaker’s contributions to a multi-participant conversation.
• Acoustics training:?Attend to the acoustical side of the business. Train the system to adapt to an acoustic environment (like the ambient noise in a call center) and speaker styles (like voice pitch, volume and pace).
• Profanity filtering:?Use filters to identify certain words or phrases and sanitize speech output

Speech recognition algorithms

The complexities of human speech have made development difficult. It is regarded as one of the most difficult areas of computer science, involving linguistics, mathematics, and statistics. Speech recognizers are composed of several components, including speech input, feature extraction, feature vectors, a decoder, and a word output. To determine the appropriate output, the decoder employs acoustic models, a pronunciation dictionary, and language models.

Speech recognition technology is evaluated on its accuracy rate, i.e. word error rate (WER), and speed. A number of factors can impact word error rate, such as pronunciation, accent, pitch, volume, and background noise. Reaching human parity - meaning an error rate on par with that of two humans speaking has long been the goal of speech recognition systems.?

Various algorithms and computation techniques are used to recognize speech into text and improve the accuracy of transcription. Below are brief explanations of some of the most commonly used methods:

Natural language processing (NLP):?

While natural language processing (NLP) is not necessarily a specific algorithm used in speech recognition, it is the branch of artificial intelligence that focuses on the interaction between humans and machines through language via speech and text. Many mobile devices incorporate speech recognition into their systems to conduct voice searches (e.g., Siri) or to improve texting accessibility.

Hidden markov models (HMM):?

Hidden Markov Models are based on the Markov chain model, which states that the probability of a given state is determined by its current state rather than its prior states. While a Markov chain model is useful for observable events such as text inputs, hidden markov models allow us to incorporate hidden events into a probabilistic model such as part-of-speech tags. They are used as sequence models in speech recognition, assigning labels to each unit in the sequence (words, syllables, sentences, etc.). These labels create a mapping with the input provided, allowing it to determine the best label sequence.

N-grams:

This is the simplest type of language model (LM), which assigns probabilities to sentences or phrases. An N-gram is sequence of N-words. For example, “order the pizza” is a trigram or 3-gram and “please order the pizza” is a 4-gram. Grammar and the probability of certain word sequences are used to improve recognition and accuracy.

Neural networks:

Neural networks, which are primarily used for deep learning algorithms, process training data by simulating the interconnectivity of the human brain through layers of nodes. Every node has inputs, weights, a bias (or threshold), and an output. If the output value exceeds a certain threshold, the node "fires," or activates, sending data to the next layer of the network. Through supervised learning, neural networks learn this mapping function, adjusting based on the loss function via gradient descent. While neural networks are more accurate and can accept more data, they have a lower performance efficiency because they are slower to train than traditional language models.

Speaker Diarization (SD):

Speaker diarization algorithms identify and segment speech by speaker identity. This helps programs better distinguish individuals in a conversation and is frequently applied at call centers distinguishing customers and sales agents.

Speech recognition use cases

A wide number of industries are utilizing different applications of speech technology today, helping businesses and consumers save time and even lives. Some examples include:

Automotive:?Speech recognizers improves driver safety by enabling voice-activated navigation systems and search capabilities in car radios.

Technology:?Virtual agents?are increasingly becoming integrated within our daily lives, particularly on our mobile devices. We use voice commands to access them through our smartphones, such as through Google Assistant or Apple’s Siri, for tasks, such as voice search, or through our speakers, via Amazon’s Alexa or Microsoft’s Cortana, to play music. They’ll only continue to integrate into the everyday products that we use, fueling the “Internet of Things” movement.

Healthcare:?Doctors and nurses leverage dictation applications to capture and log patient diagnoses and treatment notes.

Sales:?Speech recognition technology has a couple of applications in sales. It can help a call center transcribe thousands of phone calls between customers and agents to identify common call patterns and issues.?AI chatbots?can also talk to people via a webpage, answering common queries and solving basic requests without needing to wait for a contact center agent to be available. It both instances speech recognition systems help reduce time to resolution for consumer issues.

Security:?As technology integrates into our daily lives, security protocols are an increasing priority. Voice-based authentication adds a viable level of security.