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Speech recognition converts spoken language into text or commands, whereas speaker recognition identifies the person speaking.
Speech recognition, also called automatic speech recognition (ASR) or speech-to-text (STT), is a branch of computational linguistics that converts spoken language into text or other interpretable forms. It supports voice user interfaces, dictation, transcription, audio search, call routing, home automation, aircraft control, accessibility tools, and pronunciation assessment. It differs from speaker recognition, which identifies who is speaking rather than what is being said. The field has progressed from early systems recognizing isolated digits or small vocabularies to large-vocabulary, speaker-independent, continuous speech recognition. Traditional systems commonly used hidden Markov models (HMMs) with acoustic and language models, while dynamic time warping was an earlier technique for matching speech sequences with different speaking speeds. Since the 2010s, deep learning methods—including deep neural networks, recurrent neural networks, long short-term memory networks, connectionist temporal classification, attention mechanisms, and transformers—have significantly improved accuracy and enabled end-to-end systems that learn multiple recognition components jointly. Core challenges include variation among speakers, accents, speaking rates, recording conditions, noise, and the need for substantial training data.
Speech recognition converts spoken language into text or commands, whereas speaker recognition identifies the person speaking.
Major applications include voice interfaces, dictation, transcription, call processing, accessibility support, pronunciation assessment, and hands-free control.
Early systems handled isolated digits or small vocabularies; later systems achieved speaker-independent and continuous large-vocabulary recognition.
Traditional ASR relied heavily on hidden Markov models, acoustic modeling, language modeling, phonetic representations, and feature normalization.
Deep learning methods such as DNNs, LSTMs, CTC, attention-based models, and transformers have substantially reduced recognition errors.
End-to-end ASR models learn acoustic, pronunciation, and language-related components together, simplifying development and deployment.
Important ongoing challenges include accents, unfamiliar speakers, background noise, recording variability, domain-specific vocabulary, and limited data for many languages.
Technology that automatically converts spoken language into written text or another machine-interpretable form.
A model that represents the relationship between speech sounds and the observed audio signal.
A model that estimates the likelihood of word or character sequences to help select plausible transcriptions.
A probabilistic model that represents speech as a sequence of hidden states producing short-time acoustic observations.
A sequence-alignment method that compares speech patterns while allowing differences in speaking speed and timing.
A neural network with multiple hidden layers that learns complex representations and relationships in speech data.
A recurrent neural network architecture designed to retain relevant information over long sequences and reduce vanishing-gradient problems.
A training method that enables sequence models to align speech inputs with transcripts when frame-level labels are unavailable.
An approach that learns to map speech signals directly to transcripts while jointly learning functions traditionally handled by separate components.
The ability of a speech recognition system to recognize speech accurately from voices not used during training.
A standard evaluation metric based on substitutions, deletions, and insertions required to transform a system transcript into the reference transcript.
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