Glossary
What is automatic speech recognition?
Definition
Automatic speech recognition (ASR) is technology that converts spoken language into written text. It captures an audio signal, analyzes its acoustic patterns, and outputs the most likely sequence of words. ASR is the listening component that lets computers, phones, and voice assistants understand what a caller says.
01How ASR works
An ASR system first digitizes incoming audio and breaks it into short frames, then extracts acoustic features that represent the sound. A model maps those features to phonemes and words, often combining acoustic and language models to weigh which word sequences are most probable. Modern systems typically use neural networks trained on large amounts of transcribed speech.
02ASR in phone and voice systems
On a phone call, ASR transcribes the caller's speech in real time so downstream software can interpret intent and respond. Telephone audio poses extra challenges such as narrowband quality, background noise, accents, and crosstalk, which can reduce accuracy. Streaming ASR is designed to return partial results quickly so the system can react without long pauses.
03Accuracy and limitations
ASR quality is often described using word error rate, which counts insertions, deletions, and substitutions against a reference transcript. Performance varies with audio quality, speaker accent, domain vocabulary, and the presence of jargon or proper nouns. Custom vocabularies and domain adaptation can improve recognition of business-specific terms like product names or street addresses.
Frequently asked questions
Is ASR the same as speech-to-text?
The terms are often used interchangeably. ASR refers to the underlying recognition technology, while speech-to-text usually describes the end-to-end task or product of turning audio into a transcript.
Does ASR understand meaning?
No. ASR only produces text from audio. Interpreting meaning, intent, or entities is handled by separate natural language understanding components.
See also
Related terms
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