Speech Recognition Meta Guide

Notes:

Speech recognition is the process of converting spoken language into text. It involves analyzing and interpreting the sounds, words, and phrases produced by a speaker and transcribing them into written form. Speech recognition systems use algorithms and models trained on large amounts of data to recognize patterns and features in the speech signal and to map them to the corresponding words and sentences. These systems can be used in a variety of applications, such as dictation, voice control of devices, voice-enabled search and navigation, automatic transcription of audio and video recordings, and more.

There are different approaches to speech recognition, including rule-based systems, which rely on predefined rules and grammars to analyze and interpret the speech, and machine learning-based systems, which use statistical models trained on large amounts of data to learn and recognize patterns in the speech signal. Modern speech recognition systems often use a combination of these approaches and can achieve high levels of accuracy in recognizing a wide range of languages and accents. However, speech recognition systems can still be challenged by factors such as background noise, accents, and speaking styles, and may require additional processing and adaptation to improve their performance in specific environments and scenarios.

Speech to text (STT) is a process that involves converting spoken language into a written or typed form. It is also known as voice to text or speech recognition. STT systems use algorithms and models trained on large amounts of data to analyze and interpret the sounds and words produced by a speaker and transcribe them into text. STT can be used in a variety of applications, such as dictation, voice control of devices, automatic transcription of audio and video recordings, and more.

STT is a subfield of natural language processing (NLP) that involves analyzing and interpreting spoken language and extracting structured information from it. It requires the use of advanced techniques in signal processing, machine learning, and linguistics to analyze and understand the nuances of human speech and to transcribe it accurately into text. STT systems can be rule-based, which rely on predefined rules and grammars to analyze the speech, or machine learning-based, which use statistical models trained on large amounts of data to learn and recognize patterns in the speech signal. Modern STT systems often use a combination of these approaches and can achieve high levels of accuracy in recognizing a wide range of languages and accents.

Wikipedia:

Category:Speech recognition software
Speech recognition
Speaker recognition (Voice biometrics)
Timeline of speech and voice recognition

References:

Automatic Speech Recognition: A Deep Learning Approach (2014)