Speech AI Companies · IVR & Voice Bots
IVR & Voice Bots Data for Speech AI Companies
Teams whose core product is speech recognition or synthesis, where dataset quality is the product roadmap and word error rate is the metric everyone watches. Deploying automated telephony flows that hold up against real Indian callers on narrowband lines.
- Buyer
- Speech AI Companies
- Use case
- IVR & Voice Bots
- Metric
- Intent accuracy
Where the two meet
WER on Indian languages is dominated by dialect and code-mixing failures that generic corpora do not cover That is a ivr & voice bots problem, and it is solved by data shaped like this:
- Telephony-bandwidth audio
- Dual-channel calls
- Intent-labelled utterances against a live taxonomy
Your evaluation criteria
- Are train/dev/test splits speaker-disjoint by construction?
- Is transcription verbatim, with disfluencies preserved?
- Is per-token language ID available for code-mixed speech?
- Is inter-annotator agreement measured and reported?
Metrics
- Intent accuracy
- Containment rate
- Barge-in handling
Pitfalls
- Studio audio downsampled to fake telephony
- Scripted callers who never interrupt
- Intent sets written by product, not derived from real calls
Contract points
- Speaker-disjoint splits guaranteed contractually
- Right to publish benchmark results
- Re-record remedy for QA failures
Frequently asked
What does a first engagement look like?
Usually a scoped pilot: one language, an evaluation set plus a first training batch, delivered in three to five weeks, followed by the full programme.
Can you match our existing vendor's schema?
Yes. Working to your schema avoids a conversion pass and keeps deliveries comparable across vendors.
How is provenance documented?
Per-item contributor records and consent mapped to IDs in the manifest.
Send your requirement
Language, volume, metric, deadline.