Anonymization, Not Elimination: Utility-Preserved Speech Anonymization
Quick summary
arXiv:2604.17000v1 Announce Type: cross Abstract: 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
Key takeaways
- arXiv:2604.17000v1 Announce Type: cross Abstract: 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
Why it matters
The significance is not only the legal text but how it changes product design. Decisions around “Anonymization, Not Elimination: Utility-Preserved Speech Anonymization” may reshape data collection, model training, output accountability and market access.
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