AI Data Companies · Speaker Diarisation
Speaker Diarisation Data for AI Data Companies
Data vendors and labelling platforms that win Indian-language work and need a delivery partner on the ground who works to their spec and under their brand. Determining who spoke when in multi-party Indian-language audio, including overlapped speech.
- Buyer
- AI Data Companies
- Use case
- Speaker Diarisation
- Metric
- Diarisation error rate
Where the two meet
Indian-language capacity is hard to build remotely, especially outside metros That is a speaker diarisation problem, and it is solved by data shaped like this:
- Per-speaker isolated channels with a mixed reference
- Genuine overlap preserved
- Turn-level ground truth
Your evaluation criteria
- Will the partner work to our specification and schema exactly?
- Is the partner willing to work white-label under our client relationship?
- Is the QA report detailed enough to hand to our client unchanged?
Metrics
- Diarisation error rate
- Overlap detection recall
- Speaker-count accuracy
Pitfalls
- Overlap edited out during recording
- Single-channel-only capture leaving no reliable ground truth
Contract points
- White-label and non-solicitation terms
- Your schema, your QA thresholds
- Predictable per-unit pricing
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.