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Voice evaluation · ASR Model Training

AI Voice Evaluation for ASR Model Training

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 asr model training, the specification is driven by one thing: word error rate overall and per dialect.

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Evaluator scoring AI voice output against a rubric — AI Voice Evaluation for ASR Model Training
Service
Voice evaluation
Use case
ASR Model Training
Primary metric
Word error rate overall and per dialect
01

Required data profile

  • Hundreds to thousands of hours of verbatim-transcribed speech
  • Wide speaker diversity: age, gender, region, education, recording condition
  • Speaker-disjoint train/dev/test splits
Voice evaluation — ASR Model Training · written into the SOW before recordingTTSMOS (1-5), MUSHRA, and A/B preference protocolsASRError typing: substitution, deletion, insertion, code-switc…PanelNative speakers of the target variety, screened and calibra…Sample sizePowered per the effect size you need to detectReportingPer-item scores plus aggregate with confidence intervalsYour values replace ours rather than being converted after delivery.
02

Technical specification

ParameterStandard
TTSMOS (1-5), MUSHRA, and A/B preference protocols
ASRError typing: substitution, deletion, insertion, code-switch failure
PanelNative speakers of the target variety, screened and calibrated
Sample sizePowered per the effect size you need to detect
ReportingPer-item scores plus aggregate with confidence intervals
Studio-grade voice recording session for text-to-speech training data — supporting ai voice evaluation for asr model training
Studio-grade voice recording session for text-to-speech training data
03

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
04

Metrics this feeds

  • Word error rate overall and per dialect
  • Entity error rate on names and numbers
  • Code-switch token accuracy
05

Failure modes to design out

  • Read-speech-only corpora that do not transfer to spontaneous audio
  • Speaker leakage across splits inflating reported accuracy
  • Normalised-only transcripts with the raw text discarded

Attention checks and reference anchors are embedded so unreliable raters are detected and excluded before analysis.

06

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 asr model training?

It covers hundreds to thousands of hours of verbatim-transcribed speech. Most asr model training 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 asr model training

Send the metric you need to move.

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