Formed in 2020, the VoicePrivacy initiative has spearheaded efforts to develop privacy-preserving solutions for speech technologies. To date, it has primarily focused on voice anonymization, i.e., transforming speech signals to conceal speaker identity while preserving speech utility. This objective has been pursued through a series of competitive benchmarking challenges, providing common datasets, standardized evaluation protocols, and meaningful metrics for fair comparison of anonymization systems. The first three editions of the VoicePrivacy Challenge (VPC) were held in 2020, 2022, and 2024. The scope of the VoicePrivacy Challenge has progressively evolved. While VPC 2020 established a foundational evaluation framework for English voice anonymization, VPC 2022 extended this framework to assess prosody preservation, and VPC 2024 further introduced explicit requirements on preserving the speaker’s emotional state. Following VPC 2024, the Attacker Challenge was introduced to foster the development of stronger attacker models, evaluated against a selection of top-performing anonymization systems submitted to VPC 2024, as well as strong baseline systems.
VoicePrivacy 2026, the fourth edition of the challenge, was scheduled from March 2026 through the VoicePrivacy Challenge workshop held in conjunction with the 6th Symposium on Security and Privacy in Speech Communication (SPSC), co-located with Interspeech 2026 in Sydney, Australia.
In keeping with prior editions, the challenge focuses on the subtask of voice anonymization, i.e., altering the speaker’s voice to conceal identity as effectively as possible while preserving linguistic content and relevant paralinguistic attributes. In VPC 2026, particular emphasis is placed on two key aspects. First, the challenge introduces stronger, domain-aware attackers optimized using domain-related data. Specifically, since most state-of-the-art anonymization approaches are based on neural voice conversion (VC) techniques, attacker models are correspondingly trained on diverse VC data to achieve stronger speaker re-identification performance. Second, beyond English anonymization, VPC 2026 explicitly extends the evaluation to a multilingual setting. The challenge is organised with two independent tracks:
The scheduled submission deadlines have passed. For late participation or questions about an existing submission, please contact the organisers before using the registration form.
VoicePrivacy 2026 is dedicated to the memory of Emmanuel Vincent, whose pioneering work and leadership were instrumental in establishing the VoicePrivacy initiative and advancing research on voice anonymization and privacy in speech technology.
Read more here.
Schedule
All submission deadlines are at 23:59 Anywhere on Earth (AoE).
Deadline
Date
Deadline for participants to submit a list for training data and models
30th April 2026
Publication of the full final list of training data and models
7th May 2026
Deadline to submit abstract
30th June 2026
Deadline for participants to submit objective evaluation results, anonymized data, and system descriptions
30th June 20264th July 2026
Submission of challenge papers to the joint SPSC Symposium and VoicePrivacy Challenge workshop
30th June 20264th July 2026
Author notification for challenge papers
18th July 2026
Joint SPSC Symposium and VoicePrivacy Challenge workshop
26th September 2026
Data
Publicly available resources are used for the training, development, and evaluation of voice anonymization systems.
Track 1: English anonymization
Development & evaluation: LibriSpeech and IEMOCAP (identical to VPC 2024)
ASV trials: Same-gender and cross-gender (mixed) trials
Emotion data: IEMOCAP dev & test
Track 2: Multilingual anonymization
Development & evaluation: Multilingual LibriSpeech (MLS) for French, Spanish, English, and German
The organisers provide baseline anonymization systems and evaluation scripts. Expand the audio samples below each baseline to compare its anonymized recordings with the same original recordings used for the participating teams.
Equal error rate (EER) — semi-informed attacker ASV
Utility
WER (ASR), UAR (SER)
Evaluation conditions
Minimum target EERs: 10%, 20%, 30%, 40%
Track 2: Multilingual anonymization
Metric
Description
Privacy
EER — averaged across French, Spanish, English, German
Utility
WER (Whisper-large-v3 ASR), UAR (emotion2vec SER) — averaged across languages
Evaluation conditions
Same as Track 1: 10%, 20%, 30%, 40%
Assessment
Higher EER → greater privacy
Lower WER → better content preservation
Higher UAR → better emotion preservation
Submissions that satisfy a given privacy requirement are ranked by utility (WER and UAR separately).
Submission
Submission deadline (passed):30th June 20264th July 2026 (AoE). Authors are kindly requested to submit abstracts by the original deadline, 30th June 2026 (AoE).
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Paper submission
All teams must submit one paper to SPSC that serves as both the scientific contribution and the official system description. No separate system description is required.
Sub-track: Track 1 (English), Track 2 (Multilingual), or Both
Team Name: use “team_name” in the submission credentials email.
How would you like this submission to be considered?
As an SPSC proceedings paper and a VoicePrivacy Challenge website publication (If accepted: proceedings + VPC website. If not accepted: VPC website only).
As a VoicePrivacy Challenge website publication only (e.g. if you plan to publish elsewhere).
Format: SPSC template, 4–6 pages excluding references; appendices allowed for technical details. Please note that submissions to the VPC paper track are not anonymized. All authors and team members should be listed in the manuscript.
Each paper must:
Include a “System description” section and/or appendix with full technical details for all submitted systems (architecture, training data, preprocessing, hyperparameters, computational cost, etc.).
Map each $anon_data_suffix to the corresponding system variant in the paper.
Summary tables (as in the evaluation plan
) are recommended.
Anonymized data and results submission
Required for all participants. Run the upload script once per system ($anon_data_suffix).
OSS_ACCESS_KEY_ID, OSS_ACCESS_KEY_SECRET, and OSS_TEAM are sent to each team separately. Test connectivity with bash 03_upload_submission_oss_track1.sh test (or track 2).
Chinese (cn_*) and Japanese (ja_*) data are optional for Track 2.
Wav files: 16 kHz, 16-bit signed integer PCM; same utterance IDs and structure as reference datasets.
<suffix> must match across result files, data directories, and paper. <TEAM_NAME> must match registration.
Ranking
Official ranking includes only rule-compliant systems for which we receive both data/results and a complete SPSC paper before the deadline. Results in the paper without a corresponding data submission are excluded.
Late submissions
SPSC paper and results submitted after the deadlines will be marked as late submission in results ranking.
Results
Results from the VoicePrivacy 2026 Challenge, presented at the joint 6th Symposium on Security and Privacy in Speech Communication and VoicePrivacy Challenge workshop in Sydney, Australia, on 26 September 2026.
Final system descriptions from the 13 participating teams, listed by Team ID. Papers and slides/posters are provided as PDFs. Presentation videos are available for seven teams. Expand Audio samples under each team to compare the original and anonymized recordings.
† DKU (T23) participated hors concours (outside the competition ranking).
FAR-CAP-DSF System for the VoicePrivacy 2026 Challenge
Emotion-Aware Speaker Anonymization with Zero-Shot TTS Foundation Models for VoicePrivacy 2026
Alef Iury Siqueira Ferreira, Lucas Hideki Ueda, Lucas Rafael Stefanel Gris, Frederico Santos de Oliveira, Augusto Seben da Rosa, Alexandre Costa Ferro Filho, Victor Moreno.
Two-Stage Speaker Anonymization with Emotion-Conditioned Resynthesis and Mild McAdams Post-Processing: A CosyVoice2 System for the VoicePrivacy 2026 Challenge
This work was conducted in the context of the Inria–NII TrustedSpeech Associate Team and was partially supported by the French National Research Agency (ANR) under the Speech Privacy project and the IPoP project of the Cybersecurity PEPR. Some baseline experiments were carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see Grid’5000).
Parts of this work were also funded by the German Research Foundation (DFG), Project: Multilingual Controllable Voice Privacy (VoiPy), Project number 533241795.
Xin Wang is partially supported by JST, PRESTO Grant Number JPMJPR23P9, Japan. Part of the baseline experiment was conducted using the TSUBAME4.0 supercomputer of Institute of Science Tokyo.
Organisers
Xiaoxiao Miao — Duke Kunshan University, China Natalia Tomashenko — Université de Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France Ridwan Arefeen — Singapore Institute of Technology, Singapore Sarina Meyer — Institute for Natural Language Processing, University of Stuttgart, Germany Michele Panariello — Audio Security and Privacy Group, EURECOM, France Xin Wang — National Institute of Informatics, Tokyo, Japan Emmanuel Vincent — Université de Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France Nicholas Evans — Audio Security and Privacy Group, EURECOM, France Junichi Yamagishi — National Institute of Informatics, Tokyo, Japan Massimiliano Todisco — Audio Security and Privacy Group, EURECOM, France
The VoicePrivacy community mourns the loss of our colleague, friend, and co-founder Emmanuel Vincent. As head of science at Inria Nancy and a driving force behind the VoicePrivacy initiative since its inception in 2020, Emmanuel shaped the field of voice anonymization through his vision, scientific rigor, and unwavering generosity toward students and collaborators.
His contributions span every edition of the challenge, from the founding evaluation framework to VoicePrivacy 2026, and his influence will continue to guide our work for years to come.
We dedicate this edition of the VoicePrivacy Challenge to his memory.
