AI Companies · Speaker Diarisation
Speaker Diarisation Data for AI Companies
Product and platform teams that need Indian-language training data on a schedule that matches their model release cycle, not a vendor's studio availability. Determining who spoke when in multi-party Indian-language audio, including overlapped speech.
- Buyer
- AI Companies
- Use case
- Speaker Diarisation
- Metric
- Diarisation error rate
Where the two meet
Model accuracy collapses on Indian accents and languages that were absent from the pretraining mix 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
- Can the partner field the speaker count and demographic quotas exactly as written?
- Is consent documented per speaker and mapped to file IDs?
- Are QA thresholds measurable and reported, or asserted?
- Can delivery be staged so training can start before the full corpus lands?
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
- Perpetual, transferable licence to the delivered data
- Clear IP assignment
- Consent that survives model distribution
- Data residency and handling
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.