Voice evaluation · Speech Emotion Recognition
AI Voice Evaluation for Speech Emotion Recognition
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 emotion recognition, the specification is driven by one thing: per-class f1.
- Service
- Voice evaluation
- Use case
- Speech Emotion Recognition
- Primary metric
- Per-class F1
Required data profile
- Elicited and natural emotional speech
- Multi-rater emotion labels with adjudication
- Balanced across emotion classes
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
- Per-class F1
- Inter-rater agreement on labels
- Escalation detection latency
Failure modes to design out
- Acted emotion only
- Single-rater labels on an inherently subjective task
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 emotion recognition?
It covers elicited and natural emotional speech. Most speech emotion recognition 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 emotion recognition
Send the metric you need to move.