Audio annotation · Speech Analytics
Audio Annotation for Speech Analytics
Labelling of existing audio: speaker diarisation, emotion, intent, events, language identification and segment-level quality tagging, against your label schema. Applied to speech analytics, the specification is driven by one thing: intent accuracy.
- Service
- Audio annotation
- Use case
- Speech Analytics
- Primary metric
- Intent accuracy
Required data profile
- Domain-realistic conversation audio
- Intent, outcome and compliance labels
- Speaker-separated channels
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
- Intent accuracy
- Compliance-event recall
- Summary factuality
Failure modes to design out
- Labels defined without listening to real calls first
- Ignoring dialect coverage in the target market
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 speech analytics?
It covers domain-realistic conversation audio. Most speech analytics 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 speech analytics
Send the metric you need to move.