Voice recording · Speaker Diarisation
Voice Recording for AI Training for Speaker Diarisation
Studio voice recording built for model training rather than broadcast: controlled acoustics, consistent mic distance, and reproducible session parameters across every speaker. Applied to speaker diarisation, the specification is driven by one thing: diarisation error rate.
- Service
- Voice recording
- Use case
- Speaker Diarisation
- Primary metric
- Diarisation error rate
Required data profile
- Per-speaker isolated channels with a mixed reference
- Genuine overlap preserved
- Turn-level ground truth
Technical specification
| Parameter | Standard |
|---|---|
| Sample rate | 48 kHz |
| Bit depth | 24-bit |
| Microphone | Large-diaphragm condenser, fixed distance, pop filter |
| Room | Treated booth, RT60 under 0.3s |
| Processing | None. No compression, EQ, or noise reduction on delivered audio |
| Session length | 30-90 minutes per speaker, with breaks logged |
Process
- Session template definition so every studio in the network records identically
- Speaker briefing and pronunciation calibration
- Take-level monitoring with immediate re-record on defect
- Per-session technical report
- Delivery with unprocessed masters
Metrics this feeds
- Diarisation error rate
- Overlap detection recall
- Speaker-count accuracy
Failure modes to design out
- Overlap edited out during recording
- Single-channel-only capture leaving no reliable ground truth
Delivered audio is never processed. Training pipelines should see the raw capture so augmentation stays under your control.
Deliverables
- Unprocessed WAV masters
- Session logs
- Take-level metadata
- Speaker consent records
Frequently asked
Is voice recording the right service for speaker diarisation?
It covers per-speaker isolated channels with a mixed reference. Most speaker diarisation programmes combine it with at least one other service; we will say so in the scope rather than selling one line item.
What languages are available?
All 14 languages in the network plus Indian English accent bands.
How is the evaluation set handled?
Collected first, from speakers disjoint from the training cohort, so improvement is measurable.
Scope voice recording for speaker diarisation
Send the metric you need to move.