# The VoicePrivacy 2020 Challenge: Results and findings

arXiv: https://arxiv.org/abs/2109.00648 · HTML: https://mlsanity.com/paper/2109.00648/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 81 references that are in the map.
- First posted on arXiv 1 Sep 2021.

## Builds on (most cited first, 21 of 21)

- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://mlsanity.com/paper/1904.02882/) (2019)
- [Federated Learning for Keyword Spotting](https://mlsanity.com/paper/1810.05512/) (2018)
- [Introducing the VoicePrivacy Initiative](https://mlsanity.com/paper/2005.01387/) (2020)
- [Speaker Anonymization Using X-vector and Neural Waveform Models](https://mlsanity.com/paper/1905.13561/) (2019)
- [Speaker anonymisation using the McAdams coefficient](https://mlsanity.com/paper/2011.01130/) (2020)
- [Evaluating Voice Conversion-based Privacy Protection against Informed Attackers](https://mlsanity.com/paper/1911.03934/) (2019)
- [Design Choices for X-vector Based Speaker Anonymization](https://mlsanity.com/paper/2005.08601/) (2020)
- [Privacy-preserving Voice Analysis via Disentangled Representations](https://mlsanity.com/paper/2007.15064/) (2020)
- [Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?](https://mlsanity.com/paper/1911.04913/) (2019)
- [The GDPR & Speech Data: Reflections of Legal and Technology Communities, First Steps towards a Common Understanding](https://mlsanity.com/paper/1907.03458/) (2019)
- [Voice-Indistinguishability: Protecting Voiceprint in Privacy-Preserving Speech Data Release](https://mlsanity.com/paper/2004.07442/) (2020)
- [Neural Harmonic-plus-Noise Waveform Model with Trainable Maximum Voice Frequency for Text-to-Speech Synthesis](https://mlsanity.com/paper/1908.10256/) (2019)
- [A Study of F0 Modification for X-Vector Based Speech Pseudonymization Across Gender](https://mlsanity.com/paper/2101.08478/) (2021)
- [Encrypted Speech Recognition using Deep Polynomial Networks](https://mlsanity.com/paper/1905.05605/) (2019)
- [Speech Pseudonymisation Assessment Using Voice Similarity Matrices](https://mlsanity.com/paper/2008.13144/) (2020)
- [VoiceMask: Anonymize and Sanitize Voice Input on Mobile Devices](https://mlsanity.com/paper/1711.11460/) (2017)
- [The Privacy ZEBRA: Zero Evidence Biometric Recognition Assessment](https://mlsanity.com/paper/2005.09413/) (2020)
- [Speaker Anonymization with Distribution-Preserving X-Vector Generation for the VoicePrivacy Challenge 2020](https://mlsanity.com/paper/2010.13457/) (2020)
- [Speaker De-identification System using Autoencoders and Adversarial Training](https://mlsanity.com/paper/2011.04696/) (2020)

## Built on by (most cited first, 30 of 46)

- [The VoicePrivacy 2024 Challenge Evaluation Plan](https://mlsanity.com/paper/2404.02677/) (2024)
- [The VoicePrivacy 2022 Challenge Evaluation Plan](https://mlsanity.com/paper/2203.12468/) (2022)
- [Differentially Private Speaker Anonymization](https://mlsanity.com/paper/2202.11823/) (2022)
- [Exact Prosody Cloning in Zero-Shot Multispeaker Text-to-Speech](https://mlsanity.com/paper/2206.12229/) (2022)
- [A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairness](https://mlsanity.com/paper/2212.09006/) (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)
- [Federated learning for secure development of AI models for Parkinson's disease detection using speech from different languages](https://mlsanity.com/paper/2305.11284/) (2023)
- [The Third VoicePrivacy Challenge: Preserving Emotional Expressiveness and Linguistic Content in Voice Anonymization](https://mlsanity.com/paper/2601.11846/) (2026)
- [Federated Learning for ASR based on Wav2vec 2.0](https://mlsanity.com/paper/2302.10790/) (2023)
- [A Benchmark for Multi-speaker Anonymization](https://mlsanity.com/paper/2407.05608/) (2024)
- [Do Not Mimic My Voice: Speaker Identity Unlearning for Zero-Shot Text-to-Speech](https://mlsanity.com/paper/2507.20140/) (2025)
- [The effect of speech pathology on automatic speaker verification -- a large-scale study](https://mlsanity.com/paper/2204.06450/) (2022)
- [OOD-Probe: A Neural Interpretation of Out-of-Domain Generalization](https://mlsanity.com/paper/2208.12352/) (2022)
- [Probing the Feasibility of Multilingual Speaker Anonymization](https://mlsanity.com/paper/2407.02937/) (2024)
- [Analysis of Speech Temporal Dynamics in the Context of Speaker Verification and Voice Anonymization](https://mlsanity.com/paper/2412.17164/) (2024)
- [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)
- [Exploratory Evaluation of Speech Content Masking](https://mlsanity.com/paper/2401.03936/) (2024)
- [Self-Supervised Speech Representations Preserve Speech Characteristics while Anonymizing Voices](https://mlsanity.com/paper/2204.01677/) (2022)
- [DeID-VC: Speaker De-identification via Zero-shot Pseudo Voice Conversion](https://mlsanity.com/paper/2209.04530/) (2022)
- [An Experimental Study on Private Aggregation of Teacher Ensemble Learning for End-to-End Speech Recognition](https://mlsanity.com/paper/2210.05614/) (2022)
- [On the Impact of Voice Anonymization on Speech Diagnostic Applications: a Case Study on COVID-19 Detection](https://mlsanity.com/paper/2304.02181/) (2023)
- [Vocoder drift compensation by x-vector alignment in speaker anonymisation](https://mlsanity.com/paper/2307.08403/) (2023)
- [Takin-VC: Expressive Zero-Shot Voice Conversion via Adaptive Hybrid Content Encoding and Enhanced Timbre Modeling](https://mlsanity.com/paper/2410.01350/) (2024)
- [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)

## Abstract (arXiv)

This paper presents the results and analyses stemming from the first VoicePrivacy 2020 Challenge which focuses on developing anonymization solutions for speech technology. We provide a systematic overview of the challenge design with an analysis of submitted systems and evaluation results. In particular, we describe the voice anonymization task and datasets used for system development and evaluation. Also, we present different attack models and the associated objective and subjective evaluation metrics. We introduce two anonymization baselines and provide a summary description of the anonymization systems developed by the challenge participants. We report objective and subjective evaluation results for baseline and submitted systems. In addition, we present experimental results for alternative privacy metrics and attack models developed as a part of the post-evaluation analysis. Finally, we summarize our insights and observations that will influence the design of the next VoicePrivacy challenge edition and some directions for future voice anonymization research.