mlsanityData to 7 Oct 2026

The VoicePrivacy 2022 Challenge Evaluation Plan

arXiv 2203.12468 ↗First posted 23 Mar 2022eess.AS cs.CL cs.CRcited by 18

Not on the map. Why?

Abstract (arXiv)

For new participants - Executive summary: (1) The task is to develop a voice anonymization system for speech data which conceals the speaker's voice identity while protecting linguistic content, paralinguistic attributes, intelligibility and naturalness. (2) Training, development and evaluation datasets are provided in addition to 3 different baseline anonymization systems, evaluation scripts, and metrics. Participants apply their developed anonymization systems, run evaluation scripts and submit objective evaluation results and anonymized speech data to the organizers. (3) Results will be presented at a workshop held in conjunction with INTERSPEECH 2022 to which all participants are invited to present their challenge systems and to submit additional workshop papers. For readers familiar with the VoicePrivacy Challenge - Changes w.r.t. 2020: (1) A stronger, semi-informed attack model in the form of an automatic speaker verification (ASV) system trained on anonymized (per-utterance) speech data. (2) Complementary metrics comprising the equal error rate (EER) as a privacy metric, the word error rate (WER) as a primary utility metric, and the pitch correlation and gain of voice distinctiveness as secondary utility metrics. (3) A new ranking policy based upon a set of minimum target privacy requirements.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 19 of its 40 references that are in the map. First posted on arXiv 23 Mar 2022.

Builds on

19 of 40 references.

References · 19

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  3. 2020HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesiscited by 1,139
  4. 2018VoxCeleb2: Deep Speaker Recognitioncited by 862
  5. 2017VoxCeleb: a large-scale speaker identification datasetcited by 714
  6. 2019LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speechcited by 639
  7. 2021VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretationcited by 455
  8. 2020Introducing the VoicePrivacy Initiativecited by 48
  9. 2021The VoicePrivacy 2020 Challenge: Results and findingscited by 45
  10. 2019Speaker Anonymization Using X-vector and Neural Waveform Modelscited by 41
  11. 2020Speaker anonymisation using the McAdams coefficientcited by 38
  12. 2019Evaluating Voice Conversion-based Privacy Protection against Informed Attackerscited by 30
7 more
  1. 2020Design Choices for X-vector Based Speaker Anonymizationcited by 27
  2. 2019The GDPR & Speech Data: Reflections of Legal and Technology Communities, First Steps towards a Common Understandingcited by 18
  3. 2022Differentially Private Speaker Anonymizationcited by 16
  4. 2022Language-Independent Speaker Anonymization Approach using Self-Supervised Pre-Trained Modelscited by 14
  5. 2019Neural Harmonic-plus-Noise Waveform Model with Trainable Maximum Voice Frequency for Text-to-Speech Synthesiscited by 10
  6. 2020Speech Pseudonymisation Assessment Using Voice Similarity Matricescited by 8
  7. 2021Benchmarking and challenges in security and privacy for voice biometricscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 9; 2025: 3; 2026: 3.

18 papers.

Sort:

  1. 2024The VoicePrivacy 2024 Challenge Evaluation Plancited by 39
  2. 2024The Impact of Speech Anonymization on Pathology and Its Limitscited by 11
  3. 2024End-to-end streaming model for low-latency speech anonymizationcited by 9
  4. 2024Privacy versus Emotion Preservation Trade-offs in Emotion-Preserving Speaker Anonymizationcited by 9
  5. 2026The Third VoicePrivacy Challenge: Preserving Emotional Expressiveness and Linguistic Content in Voice Anonymizationcited by 8
  6. 2024A Benchmark for Multi-speaker Anonymizationcited by 7
  7. 2024Probing the Feasibility of Multilingual Speaker Anonymizationcited by 5
  8. 2024Differential privacy enables fair and accurate AI-based analysis of speech disorders while protecting patient datacited by 4
  9. 2025The First VoicePrivacy Attacker Challengecited by 4
  10. 2026Self Voice Conversion as an Attack against Neural Audio Watermarkingcited by 4
  11. 2023On the Impact of Voice Anonymization on Speech Diagnostic Applications: a Case Study on COVID-19 Detectioncited by 1
  12. 2023Vocoder drift compensation by x-vector alignment in speaker anonymisationcited by 1
6 more
  1. 2025SEF-MK: Speaker-Embedding-Free Voice Anonymization through Multi-k-means Quantizationcited by 1
  2. 2023Long-term Conversation Analysis: Exploring Utility and Privacycited by 0
  3. 2024Modeling and Estimation of Vocal Tract and Glottal Source Parameters Using ARMAX-LF Modelcited by 0
  4. 2024The First VoicePrivacy Attacker Challenge Evaluation Plancited by 0
  5. 2025Perceptual implications of automatic anonymization in pathological speechcited by 0
  6. 2026A Training-Free Proactive Defense Against Partial Speech Manipulation via Self-Embedding Steganographycited by 0