Voice evaluation · Speaker Diarisation
AI Voice Evaluation for Speaker Diarisation
Human evaluation of your speech models: MOS and preference testing for TTS, WER-in-context review for ASR, and native-speaker judgement on naturalness and intelligibility. Applied to speaker diarisation, the specification is driven by one thing: diarisation error rate.
- Service
- Voice evaluation
- 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 |
|---|---|
| TTS | MOS (1-5), MUSHRA, and A/B preference protocols |
| ASR | Error typing: substitution, deletion, insertion, code-switch failure |
| Panel | Native speakers of the target variety, screened and calibrated |
| Sample size | Powered per the effect size you need to detect |
| Reporting | Per-item scores plus aggregate with confidence intervals |
Process
- Protocol design and sample-size calculation
- Panel recruitment and calibration on reference items
- Blind evaluation with attention checks
- Statistical analysis
- Report with per-error-type breakdown
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
Attention checks and reference anchors are embedded so unreliable raters are detected and excluded before analysis.
Deliverables
- Raw per-rater scores
- Aggregated results with confidence intervals
- Error-type analysis
- Recommended fix priorities
Frequently asked
Is voice evaluation 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 evaluation for speaker diarisation
Send the metric you need to move.