A context-dependent encoding of phoneme durations identifies speakers far better than average-duration vectors and remains effective on anonymized speech without retraining on anonymized data.
In this study, our objective is to discover represen- tations that more efficiently capture speaker information con- veyed by speech temporal dynamics
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Exploiting Context-dependent Duration Features for Voice Anonymization Attack Systems
A context-dependent encoding of phoneme durations identifies speakers far better than average-duration vectors and remains effective on anonymized speech without retraining on anonymized data.