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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).
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]
Modern speech recognition systems use both an acoustic model and a language model to represent the statistical properties of speech. The acoustic model models the relationship between the audio signal and the phonetic units in the language. The language model is responsible for modeling the word sequences in the language. These two models are ...
Speech recognition software is available for many computing platforms, operating systems, use models, and software licenses. Here is a listing of such, grouped in various useful ways. Here is a listing of such, grouped in various useful ways.
Psycholinguistic models of speech perception, e.g. TRACE, must be distinguished from computer speech recognition tools. The former are psychological theories about how the human mind/brain processes information. The latter are engineered solutions for converting an acoustic signal into text.
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 ...
The company has been working on speech and audio recognition technology since its founding in 2005 -- putting it in a prime position to turn large language models (LLMs) into truly conversational ...
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 ...
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