LLM Companies · Accent Adaptation
Accent Adaptation Data for LLM Companies
Foundation and applied LLM teams that need Indian-language human data with provable provenance, covering languages their web crawl barely touched. Adapting an English or multilingual model so it holds accuracy across Indian accent bands.
- Buyer
- LLM Companies
- Use case
- Accent Adaptation
- Metric
- Per-accent WER spread
Where the two meet
Web-scraped Indian-language text is thin, noisy and heavily transliterated 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
- Is every item traceable to a screened, consenting contributor?
- Can contributors be screened by domain expertise, not just language?
- Is there an adjudication process for disagreement on subjective tasks?
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
- Auditable provenance records
- Contributor consent for model training and distribution
- No third-party or scraped content in deliverables
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.