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The First VoicePrivacy Attacker Challenge Evaluation Plan

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arxiv 2410.07428 v2 pith:6KSZPDSD submitted 2024-10-09 eess.AS cs.CLcs.CR

classification eess.AScs.CLcs.CR
keywords challengeattackersystemsevaluationvoiceprivacywillanonymizationdata
verification ladder T0 review T1 audit T2 compute T3 formal
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The First VoicePrivacy Attacker Challenge is a new kind of challenge organized as part of the VoicePrivacy initiative and supported by ICASSP 2025 as the SP Grand Challenge It focuses on developing attacker systems against voice anonymization, which will be evaluated against a set of anonymization systems submitted to the VoicePrivacy 2024 Challenge. Training, development, and evaluation datasets are provided along with a baseline attacker system. Participants shall develop their attacker systems in the form of automatic speaker verification systems and submit their scores on the development and evaluation data to the organizers. To do so, they can use any additional training data and models, provided that they are openly available and declared before the specified deadline. The metric for evaluation is equal error rate (EER). Results will be presented at the ICASSP 2025 special session to which 5 selected top-ranked participants will be invited to submit and present their challenge systems.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Risks and Detection of Overestimated Privacy Protection in Voice Anonymisation

    eess.AS 2025-07 conditional novelty 7.0 of 10

    Mismatches between the anonymisation systems used to train and test speaker verification attacks can overestimate privacy protection, and a validation-vs-test gap can detect such mismatches.

  2. Exploiting Context-dependent Duration Features for Voice Anonymization Attack Systems

    cs.SD 2025-07 conditional novelty 6.0 of 10

    A context-dependent encoding of phoneme durations identifies speakers far better than average-duration vectors and remains effective on anonymized speech without retraining on anonymized data.

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