Call Centre AI Companies · Speaker Diarisation
Speaker Diarisation Data for Call Centre AI Companies
Agent-assist, QA-automation and voice-bot vendors serving Indian BPO and enterprise contact centres, working with narrowband telephony audio and heavy accent variation. Determining who spoke when in multi-party Indian-language audio, including overlapped speech.
- Buyer
- Call Centre AI Companies
- Use case
- Speaker Diarisation
- Metric
- Diarisation error rate
Where the two meet
Production audio is 8 kHz telephony; models trained on studio audio degrade sharply 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 narrowband simulated at capture, not by downsampling studio audio?
- Are agent and customer on separate channels?
- Are emotion and escalation variants available on demand?
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
- Consented synthetic-scenario audio with no real customer PII
- Scenario library ownership
- Per-scenario volume guarantees
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.