The VoicePrivacy 2020 Challenge Evaluation Plan
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Abstract (arXiv)
The VoicePrivacy Challenge 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 document, 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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 10 of its 29 references that are in the map. First posted on arXiv 14 May 2022.
Builds on
10 of 29 references.
References · 10
- 2018VoxCeleb2: Deep Speaker Recognitioncited by 862
- 2017VoxCeleb: a large-scale speaker identification datasetcited by 714
- 2019LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speechcited by 639
- 2020Introducing the VoicePrivacy Initiativecited by 48
- 2019Speaker Anonymization Using X-vector and Neural Waveform Modelscited by 41
- 2020Speaker anonymisation using the McAdams coefficientcited by 38
- 2019Evaluating Voice Conversion-based Privacy Protection against Informed Attackerscited by 30
- 2019The GDPR & Speech Data: Reflections of Legal and Technology Communities, First Steps towards a Common Understandingcited by 18
- 2019Neural Harmonic-plus-Noise Waveform Model with Trainable Maximum Voice Frequency for Text-to-Speech Synthesiscited by 10
- 2019ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speechcited by 3
Built on by
Citing papers per year: 2022: 3; 2023: 0; 2024: 1; 2025: 1; 2026: 1.
6 papers.
- 2024The VoicePrivacy 2024 Challenge Evaluation Plancited by 39
- 2022Anonymizing Speech with Generative Adversarial Networks to Preserve Speaker Privacycited by 20
- 2022A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairnesscited by 13
- 2022Are disentangled representations all you need to build speaker anonymization systems?cited by 10
- 2025Children's Voice Privacy: First Steps And Emerging Challengescited by 5
- 2026DAST: A Dual-Stream Voice Anonymization Attacker with Staged Trainingcited by 0
- 2026DAST: A Dual-Stream Voice Anonymization Attacker with Staged Training13 Mar 2026
- 2025Children's Voice Privacy: First Steps And Emerging Challenges30 May 2025
- 2024The VoicePrivacy 2024 Challenge Evaluation Plan3 Apr 2024
- 2022A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairness18 Dec 2022
- 2022Anonymizing Speech with Generative Adversarial Networks to Preserve Speaker Privacy13 Oct 2022
- 2022Are disentangled representations all you need to build speaker anonymization systems?22 Aug 2022