Listeners detect automatic anonymization in pathological speech at 91-93% accuracy with a 30-point perceived quality drop, yet clinical severity ratings stay nearly unchanged for dysarthria, dysglossia, and dysphonia.
Anonymizing speech: Evaluating and designing speaker anonymization techniques
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A two-stage framework replaces personally identifiable information via generative editing and anonymizes voices with a flow-matching model to achieve stronger privacy than VoicePrivacy baselines while keeping utility high for retrained ASR, TTS, and SER models.
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Perceptual implications of automatic anonymization in pathological speech
Listeners detect automatic anonymization in pathological speech at 91-93% accuracy with a 30-point perceived quality drop, yet clinical severity ratings stay nearly unchanged for dysarthria, dysglossia, and dysphonia.
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Anonymization, Not Elimination: Utility-Preserved Speech Anonymization
A two-stage framework replaces personally identifiable information via generative editing and anonymizes voices with a flow-matching model to achieve stronger privacy than VoicePrivacy baselines while keeping utility high for retrained ASR, TTS, and SER models.