Anonymization, Not Elimination: Utility-Preserved Speech Anonymization
The growing reliance on large-scale speech data has made privacy protection a critical concern. However, existing anonymization approaches often degrade data utility, for example by disrupting acoustic continuity or reducing vocal diversity, which compromises the value of speech data for downstream tasks such as Automatic Speech Recognition (ASR), Text-to-Speech (TTS), and Speech Emotion Recognition (SER). Current evaluation practices are also limited, as they mainly rely on direct testing of anonymized speech with pretrained models, providing only a partial view of utility. To address these issues, we propose a novel two-stage framework that protects both linguistic content and acoustic ...
| Authors | Yunchong Xiao, Yuxiang Zhao, Ziyang Ma et al. |
|---|---|
| Primary category | eess.AS |
| Published | 18 April 2026 |
| Last updated at source | 18 April 2026 |
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