Audio annotation · Voice Biometrics
Audio Annotation for Voice Biometrics
Labelling of existing audio: speaker diarisation, emotion, intent, events, language identification and segment-level quality tagging, against your label schema. Applied to voice biometrics, the specification is driven by one thing: equal error rate.
- Service
- Audio annotation
- Use case
- Voice Biometrics
- Primary metric
- Equal error rate
Required data profile
- Many sessions per speaker across days and channels
- Same-speaker channel variation
- Optional spoof and replay sets
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
- Equal error rate
- Cross-channel EER
- Spoof detection rate
Failure modes to design out
- One session per speaker, which makes intra-speaker variability unmodellable
- No channel variation
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 voice biometrics?
It covers many sessions per speaker across days and channels. Most voice biometrics 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 voice biometrics
Send the metric you need to move.