Speech AI Companies · LLM Evaluation
LLM Evaluation 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. Human evaluation of large language model output in Indian languages, including cultural and factual fit.
- Buyer
- Speech AI Companies
- Use case
- LLM Evaluation
- Metric
- Rubric scores with confidence intervals
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 llm evaluation problem, and it is solved by data shaped like this:
- Native-speaker rater panels per language
- Rubric-based scoring with calibration
- Overlapping assignments for agreement
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
- Rubric scores with confidence intervals
- Inter-rater agreement
- Failure-mode distribution
Pitfalls
- Raters who are fluent but not native in the variety
- Rubrics written in English and applied to non-English output without localisation
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.