Transcription

Transcription is the document workflow, not the recognition step that feeds it. The output is a finished record: speaker labels attached to the right people, timestamps linking text to the moment it was said, paragraph breaks, corrected spellings of names and jargon, and a glossary applied consistently. An editor sits at the center, syncing playback with text so corrections take seconds. Records export as subtitle files, word processor documents or plain text, often with chapter breaks and summaries attached.

Journalists working through interviews, qualitative researchers coding long sessions, legal and medical documentation teams, and anyone keeping meeting records are the core users, alongside compliance captioning. Comparing transcription AI tools comes down to speaker diarization accuracy, error rates on overlapping and noisy audio, custom vocabulary support, language coverage, turnaround, whether a human review tier exists, integrations with meeting platforms, and export range.

Test with a genuinely difficult recording, because names, numbers and crosstalk fail first and cost the most to fix. Decide early between verbatim text and a cleaned read, since that choice affects every downstream edit. Check retention, data residency and whether your audio trains models. Costs run per minute or hour of audio, or as a subscription allowance, with human-verified work priced far higher.