Voice Assistant Companies · Speaker Diarisation
Speaker Diarisation Data for Voice Assistant Companies
Device, OS and appliance makers shipping assistants into Indian homes and vehicles, where wake-word reliability and far-field accuracy decide the review scores. Determining who spoke when in multi-party Indian-language audio, including overlapped speech.
- Buyer
- Voice Assistant Companies
- Use case
- Speaker Diarisation
- Metric
- Diarisation error rate
Where the two meet
Wake-word false accepts and rejects spike on Indian phonetics 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 recordings be captured at the distances and conditions the device sees?
- Are entity-heavy prompt sets available (names, addresses, PIN codes, amounts)?
- Can negative wake-word data be collected alongside positives?
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
- Device-specific recording conditions
- Exclusive use of the collected wake-word data
- Staged delivery per firmware milestone
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.