ML Research Groups · Speaker Diarisation
Speaker Diarisation Data for ML Research Groups
Academic and industrial research teams building benchmarks and studying low-resource Indian languages, where documentation and reproducibility matter as much as volume. Determining who spoke when in multi-party Indian-language audio, including overlapped speech.
- Buyer
- ML Research Groups
- Use case
- Speaker Diarisation
- Metric
- Diarisation error rate
Where the two meet
Low-resource languages have no usable public data at all 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
- Is the collection protocol documented well enough to publish?
- Are speaker demographics reported in aggregate for dataset cards?
- Can the data be released openly, and under what consent terms?
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
- Open-release-compatible consent
- Dataset card material provided with delivery
- Attribution and citation terms
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.