LLM human data · Accent Adaptation
Human Data for LLM Projects for Accent Adaptation
Human-generated text and speech for LLM training and evaluation in Indian languages: prompts, preference rankings, instruction-response pairs, red-teaming and cultural-fit review. Applied to accent adaptation, the specification is driven by one thing: per-accent wer spread.
- Service
- LLM human data
- Use case
- Accent Adaptation
- Primary metric
- Per-accent WER spread
Required data profile
- Accent-band balanced speech with substrate-language tags
- Matched content across bands for controlled comparison
Technical specification
| Parameter | Standard |
|---|---|
| Task types | Prompt writing, response ranking, instruction-response pairs, adversarial testing |
| Languages | Any language in the network, including code-mixed Hinglish |
| Contributors | Screened by domain, education band and language proficiency |
| Agreement | Overlapping assignments with adjudication |
| Provenance | Per-item contributor and time records |
Process
- Task specification and rubric design
- Contributor screening against the rubric
- Calibration round with feedback
- Production with overlap and gold items
- Adjudication and delivery
Metrics this feeds
- Per-accent WER spread
- Regression on the original accent set
Failure modes to design out
- Treating Indian English as one accent
- No substrate tagging, so the model cannot be evaluated per band
Every item is traceable to a screened contributor, which matters when a model vendor audits your data provenance.
Deliverables
- Task data in your schema
- Rubric and calibration results
- Contributor metadata (anonymised)
- Agreement statistics
Frequently asked
Is llm human data the right service for accent adaptation?
It covers accent-band balanced speech with substrate-language tags. Most accent adaptation 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 llm human data for accent adaptation
Send the metric you need to move.