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Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech-to-text (STT).
A popular example, often quoted in the field, is the phrase "How to wreck a nice beach", which sounds very similar to "How to recognize speech". [4] As this example shows, proper lexical segmentation depends on context and semantics which draws on the whole of human knowledge and experience, and would thus require advanced pattern recognition ...
A speech recognition grammar is a set of word patterns, and tells a speech recognition system what to expect a human to say. For instance, if you call an auto-attendant application, it will prompt you for the name of a person (with the expectation that your call will be transferred to that person's phone). It will then start up a speech ...
Whisper is a machine learning model for speech recognition and transcription, created by OpenAI and first released as open-source software in September 2022. [2]It is capable of transcribing speech in English and several other languages, and is also capable of translating several non-English languages into English. [1]
The cohort model is based on the concept that auditory or visual input to the brain stimulates neurons as it enters the brain, rather than at the end of a word. [5] This fact was demonstrated in the 1980s through experiments with speech shadowing, in which subjects listened to recordings and were instructed to repeat aloud exactly what they heard, as quickly as possible; Marslen-Wilson found ...
TRACE is a connectionist model of speech perception, proposed by James McClelland and Jeffrey Elman in 1986. [1] It is based on a structure called "the TRACE", a dynamic processing structure made up of a network of units, which performs as the system's working memory as well as the perceptual processing mechanism. [2]
Linear predictive coding (LPC) is a speech coding method used in speaker recognition and speech verification. [citation needed] Ambient noise levels can impede both collections of the initial and subsequent voice samples. Noise reduction algorithms can be employed to improve accuracy, but incorrect application can have the opposite effect.
These models enabled more context-aware and semantically rich understanding of speech. [10] [7] In recent years, end-to-end speech recognition models have gained popularity. These models simplify the speech recognition pipeline by directly converting audio input into text output, bypassing intermediate steps like feature extraction and acoustic ...