mlsanityData to 7 Oct 2026

The VoicePrivacy 2022 Challenge: Progress and Perspectives in Voice Anonymisation

arXiv 2407.11516 ↗First posted 16 Jul 2024eess.AScited by 17

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Abstract (arXiv)

The VoicePrivacy Challenge promotes the development of voice anonymisation solutions for speech technology. In this paper we present a systematic overview and analysis of the second edition held in 2022. We describe the voice anonymisation task and datasets used for system development and evaluation, present the different attack models used for evaluation, and the associated objective and subjective metrics. We describe three anonymisation baselines, provide a summary description of the anonymisation systems developed by challenge participants, and report objective and subjective evaluation results for all. In addition, we describe post-evaluation analyses and a summary of related work reported in the open literature. Results show that solutions based on voice conversion better preserve utility, that an alternative which combines automatic speech recognition with synthesis achieves greater privacy, and that a privacy-utility trade-off remains inherent to current anonymisation solutions. Finally, we present our ideas and priorities for future VoicePrivacy Challenge editions.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jul 2024.

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Citing papers per year: 2024: 1; 2025: 8; 2026: 8.

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  1. 2026The Third VoicePrivacy Challenge: Preserving Emotional Expressiveness and Linguistic Content in Voice Anonymizationcited by 8
  2. 2025Do Not Mimic My Voice: Speaker Identity Unlearning for Zero-Shot Text-to-Speechcited by 6
  3. 2025The First VoicePrivacy Attacker Challengecited by 4
  4. 2025LinearVC: Linear transformations of self-supervised features through the lens of voice conversioncited by 3
  5. 2025Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunitiescited by 2
  6. 2025You Are What You Say: Exploiting Linguistic Content for VoicePrivacy Attackscited by 1
  7. 2025SEF-MK: Speaker-Embedding-Free Voice Anonymization through Multi-k-means Quantizationcited by 1
  8. 2025SegReConcat: A Data Augmentation Method for Voice Anonymization Attackcited by 1
  9. 2024The First VoicePrivacy Attacker Challenge Evaluation Plancited by 0
  10. 2025Towards the Anonymization of the Language Modelingcited by 0
  11. 2026StreamVoiceAnon+: Emotion-Preserving Streaming Speaker Anonymization via Frame-Level Acoustic Distillationcited by 0
  12. 2026Privacy-Preserving End-to-End Full-Duplex Speech Dialogue Modelscited by 0
5 more
  1. 2026DiffAnon: Diffusion-based Prosody Control for Voice Anonymizationcited by 0
  2. 2026Child-Centric Voice Anonymization in Single and Multi-Speaker Speech via Domain-Adapted SSL Modelscited by 0
  3. 2026Your Voice Cloning System is Secretly a Voice Anonymizercited by 0
  4. 2026Beyond EER: Multi-Dimensional Evaluation of Information Leakage in Speaker De-Identificationcited by 0
  5. 2026Forget who you Forgot: Speaker Unlearning to Prevent Re-Identification in Zero-Shot Text-to-Speechcited by 0