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Relevance 7/10Audio and SpeechIntermediate7 min read
Speaker Diarization Labeling
Speaker diarization labeling identifies who spoke when in audio streams.
Why it matters for annotators
Diarization quality is key for meeting transcription and conversation AI needs.
Visual mental model
Audio timeline -> speaker segments -> identity tags.
Examples (bad vs good)
Scenario: Real annotation scenario involving Speaker Diarization Labeling
Bad: Labeling quickly without applying project rubric.
Good: Applying rubric criteria, documenting rationale, and escalating uncertainty.
Common mistakes
- Skipping guideline details for edge cases.
- Applying inconsistent criteria across similar samples.
- Avoiding escalation even when uncertain.
Submission checklist
- Read the latest guideline update before each batch.
- Apply rubric dimensions explicitly in each decision.
- Escalate ambiguous items with concise rationale.