Audio Transcription Service

Match the method to the source

Single-speaker voice memos and dictation are the easiest case. Even a quiet phone recording in a small room transcribes cleanly, and speaker diarization has nothing to separate.

Two-person conversations are where most tools earn their keep. The hard part is not vocabulary, it is turn-taking — overlapping speech, interruptions, and crosstalk are where automated transcription actually loses accuracy.

Panel, lecture, or conference recordings are the hardest. Room acoustics blur speaker boundaries, and the error compounds over an hour: a misattributed line at minute four makes everything after it suspect.

Know which case you have before you judge the output. A transcript that looks wrong to you may be a tool doing the best available on a genuinely difficult recording.

What accuracy actually looks like

Headline accuracy claims of 95%+ are usually measured on clean, read speech from native speakers in quiet conditions. Real-world recordings are noisier than the test set.

Rough expectations for clear English in ordinary conditions: clean voice memo around 97-99%, phone call with one overlap around 92-95%, multi-speaker meeting in a live room 85-92%. Diarization accuracy is always lower than word accuracy, and falls further as the speaker count rises.

Judge a transcript against its input, not against the marketing claim.

Privacy and where files go

Decide this before you upload anything you were not permitted to send to a third party.

Consent matters first: recording a conversation without telling the people in it is a legal and professional problem no encryption setting fixes. If you are transcribing other people's voices, tell them.

Then check retention. Whether the provider deletes uploads after processing, and whether your transcript is stored or discarded, is a property you need confirmed — not assumed from a privacy policy that describes the marketing site.

Getting the most out of a second pass

Auto-transcription output benefits from a fast human pass more than any other stage of the workflow. Ninety seconds of skimming catches the errors that actually matter: wrong names, wrong numbers, wrong technical terms.

Mark terms the engine will reliably get wrong — product names, acronyms, surnames — and substitute them after. Doing this once as a find-and-replace is far faster than proofreading every sentence for accuracy you were never going to reach anyway.