# Introducing the VoicePrivacy Initiative

arXiv: https://arxiv.org/abs/2005.01387 · HTML: https://mlsanity.com/paper/2005.01387/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 30 of 49)

- [The VoicePrivacy 2020 Challenge: Results and findings](https://mlsanity.com/paper/2109.00648/) (2021)
- [The VoicePrivacy 2024 Challenge Evaluation Plan](https://mlsanity.com/paper/2404.02677/) (2024)
- [Anonymizing Speech with Generative Adversarial Networks to Preserve Speaker Privacy](https://mlsanity.com/paper/2210.07002/) (2022)
- [The VoicePrivacy 2022 Challenge Evaluation Plan](https://mlsanity.com/paper/2203.12468/) (2022)
- [Differentially Private Speaker Anonymization](https://mlsanity.com/paper/2202.11823/) (2022)
- [Improving speaker de-identification with functional data analysis of f0 trajectories](https://mlsanity.com/paper/2203.16738/) (2022)
- [Resource-Efficient Separation Transformer](https://mlsanity.com/paper/2206.09507/) (2022)
- [The Impact of Speech Anonymization on Pathology and Its Limits](https://mlsanity.com/paper/2404.08064/) (2024)
- [Privacy attacks for automatic speech recognition acoustic models in a federated learning framework](https://mlsanity.com/paper/2111.03777/) (2021)
- [Speaker Anonymization with Phonetic Intermediate Representations](https://mlsanity.com/paper/2207.04834/) (2022)
- [Are disentangled representations all you need to build speaker anonymization systems?](https://mlsanity.com/paper/2208.10497/) (2022)
- [Privacy versus Emotion Preservation Trade-offs in Emotion-Preserving Speaker Anonymization](https://mlsanity.com/paper/2409.03655/) (2024)
- [Learning De-identified Representations of Prosody from Raw Audio](https://mlsanity.com/paper/2107.08248/) (2021)
- [The Third VoicePrivacy Challenge: Preserving Emotional Expressiveness and Linguistic Content in Voice Anonymization](https://mlsanity.com/paper/2601.11846/) (2026)
- [Configurable Privacy-Preserving Automatic Speech Recognition](https://mlsanity.com/paper/2104.00766/) (2021)
- [Federated Learning for ASR based on Wav2vec 2.0](https://mlsanity.com/paper/2302.10790/) (2023)
- [SAIC: Integration of Speech Anonymization and Identity Classification](https://mlsanity.com/paper/2312.15190/) (2023)
- [DarkStream: real-time speech anonymization with low latency](https://mlsanity.com/paper/2509.04667/) (2025)
- [The VoicePrivacy 2020 Challenge Evaluation Plan](https://mlsanity.com/paper/2205.07123/) (2022)
- [Protecting gender and identity with disentangled speech representations](https://mlsanity.com/paper/2104.11051/) (2021)
- [Generating gender-ambiguous voices for privacy-preserving speech recognition](https://mlsanity.com/paper/2207.01052/) (2022)
- [Analysis of Speech Temporal Dynamics in the Context of Speaker Verification and Voice Anonymization](https://mlsanity.com/paper/2412.17164/) (2024)
- [TVTSyn: Content-Synchronous Time-Varying Timbre for Streaming Voice Conversion and Anonymization](https://mlsanity.com/paper/2602.09389/) (2026)
- [On the invertibility of a voice privacy system using embedding alignement](https://mlsanity.com/paper/2110.05431/) (2021)
- [Fantomas: Understanding Face Anonymization Reversibility](https://mlsanity.com/paper/2210.10651/) (2022)
- [Differential privacy enables fair and accurate AI-based analysis of speech disorders while protecting patient data](https://mlsanity.com/paper/2409.19078/) (2024)
- [The First VoicePrivacy Attacker Challenge](https://mlsanity.com/paper/2504.14183/) (2025)
- [Sotto Voce: Federated Speech Recognition with Differential Privacy Guarantees](https://mlsanity.com/paper/2207.07816/) (2022)
- [Prosody-Driven Privacy-Preserving Dementia Detection](https://mlsanity.com/paper/2407.03470/) (2024)
- [Exploiting Context-dependent Duration Features for Voice Anonymization Attack Systems](https://mlsanity.com/paper/2507.15214/) (2025)

## Abstract (arXiv)

The VoicePrivacy initiative aims to promote the development of privacy preservation tools for speech technology by gathering a new community to define the tasks of interest and the evaluation methodology, and benchmarking solutions through a series of challenges. In this paper, we formulate the voice anonymization task selected for the VoicePrivacy 2020 Challenge and describe the datasets used for system development and evaluation. We also present the attack models and the associated objective and subjective evaluation metrics. We introduce two anonymization baselines and report objective evaluation results.