Definition: Speech-to-text, also called automatic speech recognition (ASR), converts spoken audio into written text, either in real time as someone speaks or in batches from recorded files.
Modern systems use neural models trained on large amounts of transcribed audio, such as OpenAI Whisper and commercial cloud APIs. Features often include speaker diarization (who spoke when), timestamps, punctuation, custom vocabulary and language detection.
Speech-to-text powers voice agents, call center analytics, meeting notes, captioning and voice search. In a voice agent it is the first step: speech becomes text, a language model decides on a response, and text-to-speech speaks it back.
Accuracy is usually measured as word error rate on your own audio, since accents, background noise, phone-line quality and domain terms change results. Real-time use cases also need low latency so conversations feel natural, which often means streaming partial transcripts as the caller speaks.
Ryz AI voice agent developers have built production voice systems, including an AI voice platform with 1M+ outbound calls, working on client teams.
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