Voice evaluation · Speech Analytics
AI Voice Evaluation for Speech Analytics
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 speech analytics, the specification is driven by one thing: intent accuracy.
- Service
- Voice evaluation
- Use case
- Speech Analytics
- Primary metric
- Intent accuracy
Required data profile
- Domain-realistic conversation audio
- Intent, outcome and compliance labels
- Speaker-separated channels
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
- Intent accuracy
- Compliance-event recall
- Summary factuality
Failure modes to design out
- Labels defined without listening to real calls first
- Ignoring dialect coverage in the target market
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 speech analytics?
It covers domain-realistic conversation audio. Most speech analytics 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 speech analytics
Send the metric you need to move.