Speech AI Companies · Accent Adaptation
Accent Adaptation 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. Adapting an English or multilingual model so it holds accuracy across Indian accent bands.
- Buyer
- Speech AI Companies
- Use case
- Accent Adaptation
- Metric
- Per-accent WER spread
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 accent adaptation problem, and it is solved by data shaped like this:
- Accent-band balanced speech with substrate-language tags
- Matched content across bands for controlled comparison
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
- Per-accent WER spread
- Regression on the original accent set
Pitfalls
- Treating Indian English as one accent
- No substrate tagging, so the model cannot be evaluated per band
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.