Speech AI Companies · Speech Analytics
Speech Analytics 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. Mining Indian-language call and meeting audio for intent, compliance and quality signals.
- Buyer
- Speech AI Companies
- Use case
- Speech Analytics
- 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 speech analytics problem, and it is solved by data shaped like this:
- Domain-realistic conversation audio
- Intent, outcome and compliance labels
- Speaker-separated channels
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
- Compliance-event recall
- Summary factuality
Pitfalls
- Labels defined without listening to real calls first
- Ignoring dialect coverage in the target market
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.