Audio annotation · Speaker Diarisation
Audio Annotation for Speaker Diarisation
Labelling of existing audio: speaker diarisation, emotion, intent, events, language identification and segment-level quality tagging, against your label schema. Applied to speaker diarisation, the specification is driven by one thing: diarisation error rate.
- Service
- Audio annotation
- Use case
- Speaker Diarisation
- Primary metric
- Diarisation error rate
Required data profile
- Per-speaker isolated channels with a mixed reference
- Genuine overlap preserved
- Turn-level ground truth
Technical specification
| Parameter | Standard |
|---|---|
| Label types | Diarisation, emotion, intent, events, language ID, quality |
| Granularity | Segment, utterance, or frame-level boundaries |
| Schema | Yours, or authored with you before work starts |
| Agreement | Multi-annotator overlap on a defined percentage |
| Tooling | Client tooling supported; otherwise our annotation workflow |
Process
- Schema definition and edge-case documentation
- Annotator training and gold-set calibration
- Production annotation with gold items seeded in
- Adjudication of disagreements by a senior reviewer
- Delivery with per-label agreement statistics
Metrics this feeds
- Diarisation error rate
- Overlap detection recall
- Speaker-count accuracy
Failure modes to design out
- Overlap edited out during recording
- Single-channel-only capture leaving no reliable ground truth
Gold items are seeded throughout production so drift is caught during the run, not at delivery.
Deliverables
- Labelled data in your schema
- Gold set and calibration results
- Per-label agreement statistics
- Edge-case log
Frequently asked
Is audio annotation the right service for speaker diarisation?
It covers per-speaker isolated channels with a mixed reference. Most speaker diarisation programmes combine it with at least one other service; we will say so in the scope rather than selling one line item.
What languages are available?
All 14 languages in the network plus Indian English accent bands.
How is the evaluation set handled?
Collected first, from speakers disjoint from the training cohort, so improvement is measurable.
Scope audio annotation for speaker diarisation
Send the metric you need to move.