A transcript mixes two speakers: how do you assess diarization reliability?

Mark speaker changes and overlapping speech first

This applies to one label containing several people, one person changing labels across segments and short replies assigned to the preceding speaker. Preserve audio and timestamps with enough context. Text alone rarely identifies a speaker, and a sentence fitting someone’s job is not evidence for assigning their name.

Separate segment labels from real-name assignments

Confirm diarization was enabled and supported by the current model and language. Inspect channels: separate microphones and mixed mono recordings provide different evidence. Overlap, similar voices and short utterances create difficulty. A configured speaker count is a processing condition, not proof of actual attendance or identity.

Repair supported local attribution and preserve uncertainty

Correct only supported local attribution against audio and store anonymous labels separately from names. Cross-segment name mapping needs known speech evidence rather than automatically matching order of appearance to an attendee list. Preserve unknown or overlapping speech and supply reviewed attribution, not guessed labels, to summarization.

Check interruptions, short replies and label consistency

Replay handovers, interruptions and one-word replies and inspect consistent labels across the recording, sampling long passages as well. Evaluate transcription and attribution separately; correct words do not establish the correct speaker. Keeping unresolved speech unassigned is part of reliability, not a defect to hide by forcing a name onto every label.

This article analyzed no private audio or established anyone’s identity. Diarization is not speaker identity authentication and cannot prove a legal or business commitment. Ask participants when audio is inadequate. Summaries must retain uncertainty; replacing numbered labels with names does not establish verified attribution.