Automatic Speech Recognition

Speech Recognition Fundamentals

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.

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Speech Recognition Applications

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