Your AI transcript says “Speaker 1.” Who is that, exactly?

A speaker label separates voices in a transcript. Keep it separate from a person’s confirmed name before assigning a statement or action.

Google’s speech guide describes diarization as distinguishing voices and tagging words by speaker number. That operation does not itself establish a person’s real name. Confirm a name mapping from permitted evidence rather than filling it from the attendee list.

An attendee list is not a mapping

If two names attended and the transcript contains two numbered speakers, the counts alone do not establish which number belongs to which name. Keep the labels unresolved until there is a reliable introduction, a confirmed mapping or another appropriate reference.

Our invented attribution check

In a fictional transcript, Speaker 1 says “I can review it” and Speaker 2 says “I will draft it.” We applied an explicitly supplied mapping 1→Mina, 2→Joon and checked that the review and drafting statements stayed with the correct labels. Reversing the mapping changed the attributed people without changing the words.

Give AI an uncertainty rule

Suggested input: “Keep speaker numbers and timestamps. Use names only from this confirmed mapping. Preserve the distinction between an offer and a commitment; mark unmapped statements for review.” Do not invent a name to make the minutes look complete.

Return to the relevant segment

Where recording and review are permitted, inspect the segment behind an important attribution and ask the relevant participants to confirm unresolved responsibilities. A fluent summary cannot settle an uncertain speaker label. Also check the service’s documented language support for diarization.

Published October 14, 2026. We tested only an original text fixture and a supplied/reversed label mapping. No recording, voice identification, transcription API, meeting, participant data or AI summary was tested.

Sources: Google · Speaker diarization.