What is speaker diarization? Who-said-what, explained

Speaker diarization is the feature that turns a wall of transcribed text into a readable conversation — and the word nobody outside the industry uses.

Short answer: Diarization is the automatic labeling of who spoke when in a recording. Instead of one undifferentiated block of text, you get [00:14:32] Speaker 2: … — which is what makes transcripts of interviews, meetings and podcasts actually usable. In AI Transcribe it's on by default, together with timestamps.

How it works, in one paragraph

The engine analyzes voice characteristics — pitch, timbre, speaking patterns — and clusters the audio into "this voice" and "that voice," then assigns each transcript segment to a cluster. It identifies that speakers differ, not who they are: labels come out as Speaker 1/Speaker 2, which you can mentally (or manually) map to names.

When it works well — and when it doesn't

Works well: two or three clearly distinct voices, decent audio, people mostly taking turns. Typical interviews and small meetings are ideal.

Degrades with: heavy crosstalk (people talking over each other), very similar-sounding voices, distant microphones, and large groups. The words usually survive; the labels are what suffer — expect an occasional attribution fix in a heated six-person debate.

Language gaps: diarization engines don't cover every transcription language. In AI Transcribe, speaker labels are currently unavailable for Hindi, Japanese, Chinese, Korean and Vietnamese — transcription and timestamps still work in all of them.

Why it matters for real work

• Interviews: quotes need attribution — see the journalist workflow.
• Meetings: "who committed to what" is the entire value of meeting notes.
• Research: qualitative coding needs per-speaker segments.

Frequently asked questions

Can it recognize people by name? No — it separates voices, it doesn't identify them. Some meeting-bot tools map labels to calendar participants; file-based transcription can't know who Speaker 2 is.

Does diarization change transcription accuracy? The words are transcribed either way; diarization adds structure. Conditions bad enough to break labels (crosstalk, noise) also hurt word accuracy — same enemy, two victims.

How many speakers can it handle? Reliably: a handful of distinct voices. Beyond that, expect merged or swapped labels in dense discussion.