2026· International Conference on Language Resources and Evaluation· pp. 5666-5676· 0 citations· 43 references
Computer Science
TL;DR
Fine-tuning on anonymized aphasic speech data improves ASR performance by 18 % compared to the performance of generic models on non-anonymized speech, and this gain in utility is achieved alongside substantial privacy protection.
Speech anonymization is evaluated with automatic speaker verification attackers whose performance depends on the attribute distribution of both their training and evaluation data, which may bias privacy estimates for some speaker groups. To alleviate this issue, we introduce VoxAnon, a dataset built from VoxCeleb1 and...
Orane Dufour, Paul Magron, Mickael Rouvier· 0 citations
As the use of smart devices continues to increase, their potential to capture sensitive speech content raises growing privacy concerns. It is therefore critical to develop techniques that prevent information leakage while preserving the utility of the audio, and evaluation metrics that accurately quantify the level of...
A. Rajasekhar, Jule Pohlhausen, Nayana Jacob Alappattu et al.· 0 citations
VoxTubeS is presented, a family of speaker-anonymized synthetic speech corpora designed for redistribution, comprising three method families and seven variants derived from the VoxTube corpus, which is distributed under CC BY-NC-SA 4.0.
This work introduces speaker differential privacy, a formal definition of speaker anonymization based on the framework of differential privacy, and a mechanism for speaker anonymization that provably satisfies the definition.
Ivoline C. Ngong, Jack D'Iorio, Hailey Schoppe et al.· 0 citations
Attacker ASV systems for voice anonymization have been studied primarily in English, leaving their behavior in multilingual settings largely unexplored. Conventional ASV has shown that both acoustic and contextual information are important for multilingual speaker verification. Inspired by this, we investigate whether...
Ridwan Arefeen, Ze Li, Rong Tong et al.· 0 citations
This work proposes repurposing XTTSv2, a multilingual voice cloning model trained on 27k hours of speech, for speaker anonymization without retraining, and introduces an iterative refinement strategy that balances privacy and utility by maximizing a harmonic mean of speaker dissimilarity and intelligibility.
Romolo Muletta, Felix Matthias Saaro, Mark Cieliebak et al.· 0 citations
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