REVIEW 2 cited by
The First VoicePrivacy Attacker Challenge Evaluation Plan
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
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.
Forward citations
Cited by 2 Pith papers
-
The Risks and Detection of Overestimated Privacy Protection in Voice Anonymisation
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.
-
Exploiting Context-dependent Duration Features for Voice Anonymization Attack Systems
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.
Discussion (0). Continue with ORCID to comment.