What Is Speaker Diarization? How AI Meeting Transcription Tells Speakers Apart

Speaker diarization is the process of automatically segmenting an audio stream by speaker identity — answering the question 'who spoke when' in a conversation with multiple participants. It is what transforms a wall of transcribed text into a readable, speaker-labeled meeting record.

How speaker diarization works

Diarization systems use acoustic modeling to identify distinct voice characteristics — pitch, cadence, timbre — and segment the audio into speaker turns. The output is typically labeled with speaker IDs (Speaker 1, Speaker 2) which can then be matched to participant names when that mapping is available.

Why it matters for meeting transcription

• A transcript without speaker labels is hard to read — it is not clear who is making each point or commitment.
• Speaker labels make it easier to identify action item owners — 'Dan will send the proposal by Friday' is a different record from an unattributed sentence.
• Coaching use cases depend on speaker separation — you cannot analyze a rep's talk ratio without knowing which audio belongs to which participant.

How MeetOye handles speaker labeling

MeetOye's Oya assistant produces speaker-labeled transcripts. Because MeetOye knows which participant is speaking (via the meeting platform's participant data), speaker labels can be matched to actual names rather than generic identifiers — producing a more readable and actionable record than systems that rely on voice characteristics alone.