Speech-to-Text

Automatic speech recognition became genuinely reliable ahead of most other AI capabilities, and it is now embedded almost everywhere: meeting notes, captions, voice input, call analytics, media search.

Accuracy is high on clear speech and degrades in predictable ways — background noise, crosstalk, strong accents underrepresented in training data, and specialist vocabulary. Speaker separation in multi-person recordings remains meaningfully harder than transcription itself.

A quiet risk with model-based transcription is that errors are fluent. Where older systems produced obvious garbage on unclear audio, a modern model may produce a plausible sentence that was never said — the same failure shape as hallucination, and harder to spot precisely because it reads naturally.

Transcription converts speech — a huge share of business communication — into something searchable and analyzable. The fluent-error property matters most where transcripts become records: medical, legal and compliance settings need verification against the audio rather than trust in the text.

Supply domain vocabulary where the tool allows it — names, product terms and acronyms are the common error source. For anything that becomes a record, spot-check against the audio rather than accepting the transcript.