# The VoicePrivacy 2022 Challenge: Progress and Perspectives in Voice Anonymisation

arXiv: https://arxiv.org/abs/2407.11516 · HTML: https://mlsanity.com/paper/2407.11516/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2024.

## Built on by (most cited first, 17 of 17)

- [The Third VoicePrivacy Challenge: Preserving Emotional Expressiveness and Linguistic Content in Voice Anonymization](https://mlsanity.com/paper/2601.11846/) (2026)
- [Do Not Mimic My Voice: Speaker Identity Unlearning for Zero-Shot Text-to-Speech](https://mlsanity.com/paper/2507.20140/) (2025)
- [The First VoicePrivacy Attacker Challenge](https://mlsanity.com/paper/2504.14183/) (2025)
- [LinearVC: Linear transformations of self-supervised features through the lens of voice conversion](https://mlsanity.com/paper/2506.01510/) (2025)
- [Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities](https://mlsanity.com/paper/2502.00451/) (2025)
- [You Are What You Say: Exploiting Linguistic Content for VoicePrivacy Attacks](https://mlsanity.com/paper/2506.09521/) (2025)
- [The First VoicePrivacy Attacker Challenge Evaluation Plan](https://mlsanity.com/paper/2410.07428/) (2024)
- [SEF-MK: Speaker-Embedding-Free Voice Anonymization through Multi-k-means Quantization](https://mlsanity.com/paper/2508.07086/) (2025)
- [SegReConcat: A Data Augmentation Method for Voice Anonymization Attack](https://mlsanity.com/paper/2508.18907/) (2025)
- [StreamVoiceAnon+: Emotion-Preserving Streaming Speaker Anonymization via Frame-Level Acoustic Distillation](https://mlsanity.com/paper/2603.06079/) (2026)
- [Privacy-Preserving End-to-End Full-Duplex Speech Dialogue Models](https://mlsanity.com/paper/2603.08179/) (2026)
- [Towards the Anonymization of the Language Modeling](https://mlsanity.com/paper/2501.02407/) (2025)
- [DiffAnon: Diffusion-based Prosody Control for Voice Anonymization](https://mlsanity.com/paper/2604.26281/) (2026)
- [Child-Centric Voice Anonymization in Single and Multi-Speaker Speech via Domain-Adapted SSL Models](https://mlsanity.com/paper/2606.29897/) (2026)
- [Your Voice Cloning System is Secretly a Voice Anonymizer](https://mlsanity.com/paper/2608.27360/) (2026)
- [Beyond EER: Multi-Dimensional Evaluation of Information Leakage in Speaker De-Identification](https://mlsanity.com/paper/2609.18673/) (2026)
- [Forget who you Forgot: Speaker Unlearning to Prevent Re-Identification in Zero-Shot Text-to-Speech](https://mlsanity.com/paper/2609.27399/) (2026)

## 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.