Audio annotation · IVR & Voice Bots
Audio Annotation for IVR & Voice Bots
Labelling of existing audio: speaker diarisation, emotion, intent, events, language identification and segment-level quality tagging, against your label schema. Applied to ivr & voice bots, the specification is driven by one thing: intent accuracy.
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- Service
- Audio annotation
- Use case
- IVR & Voice Bots
- Primary metric
- Intent accuracy
01
Required data profile
- Telephony-bandwidth audio
- Dual-channel calls
- Intent-labelled utterances against a live taxonomy
02
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 |
03
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
04
Metrics this feeds
- Intent accuracy
- Containment rate
- Barge-in handling
05
Failure modes to design out
- Studio audio downsampled to fake telephony
- Scripted callers who never interrupt
- Intent sets written by product, not derived from real calls
Gold items are seeded throughout production so drift is caught during the run, not at delivery.
06
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 ivr & voice bots?
It covers telephony-bandwidth audio. Most ivr & voice bots 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 ivr & voice bots
Send the metric you need to move.