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Voice evaluation · Machine Translation

AI Voice Evaluation for Machine Translation

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 machine translation, the specification is driven by one thing: human adequacy and fluency scores.

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Abstract visualisation of translation between two Indian languages — AI Voice Evaluation for Machine Translation
Service
Voice evaluation
Use case
Machine Translation
Primary metric
Human adequacy and fluency scores
01

Required data profile

  • Sentence-aligned parallel corpora
  • Register-matched to your product
  • Enforced terminology glossary
Voice evaluation — Machine Translation · 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
Speaker reading a prompt script into a studio microphone — supporting ai voice evaluation for machine translation
Speaker reading a prompt script into a studio microphone
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

  • Human adequacy and fluency scores
  • Terminology compliance rate
  • Back-translation divergence
05

Failure modes to design out

  • Pivoting everything through English
  • Post-edited machine output passed off as human translation

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 machine translation?

It covers sentence-aligned parallel corpora. Most machine translation 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 machine translation

Send the metric you need to move.

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