Voice evaluation · Voice Biometrics
AI Voice Evaluation for Voice Biometrics
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 voice biometrics, the specification is driven by one thing: equal error rate.
- Service
- Voice evaluation
- Use case
- Voice Biometrics
- Primary metric
- Equal error rate
Required data profile
- Many sessions per speaker across days and channels
- Same-speaker channel variation
- Optional spoof and replay sets
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
- Equal error rate
- Cross-channel EER
- Spoof detection rate
Failure modes to design out
- One session per speaker, which makes intra-speaker variability unmodellable
- No channel variation
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 voice biometrics?
It covers many sessions per speaker across days and channels. Most voice biometrics 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 voice biometrics
Send the metric you need to move.