The human-readable names attached to each utterance in a transcript — "Alice," "Bob," "Customer" — as opposed to the raw "Speaker 1, Speaker 2" labels that diarization produces by default. Mapping anonymous speaker clusters to real names is a separate step from diarization itself, and there are several approaches: matching speakers to the calendar invite (works when the participant list is short and known), one-time voice enrollment (a participant speaks a brief sample once, and their voice is recognized in future meetings), or post-hoc manual labeling. Speaker labels make transcripts dramatically more useful — a recap that says "Alice committed to the timeline" is far more actionable than "Speaker 3 committed to the timeline."