10 citations · 11 across the 16 of their papers we have counts for
13 papers · 1 filter
Preemptive defense against re-identification attacks on voice anonymization via adversarial perturbation
Michele Panariello, Yibo Bai, Massimiliano Todisco +1
Voice anonymization (VA) is used to conceal the voice identities in speech data. Performance is usually estimated using automatic speaker verification (ASV) as a proxy to judge the…
Latent Secret Spin: Keyed Orthogonal Rotations for Blind Speech Watermarking in Anisotropic Latent Spaces
Emma Coletta, Massimiliano Todisco, Michele Panariello +2
We introduce Latent Secret Spin (LSS), a blind speech watermarking method based on geometric operations in codec latent space. Based upon orthogonal rotations to principal componen…
Evaluating voice anonymisation using similarity rank disclosure
Shilpa Chandra, Matteo Pettenò, Nicholas Evans +7
The evaluation of voice anonymisation remains challenging. Current practice relies on automatic speaker verification metrics such as the equal error rate (EER). Performance estimat…
MDD: a Mask Diffusion Detector to Protect Speaker Verification Systems from Adversarial Perturbations
Yibo Bai, Sizhou Chen, Michele Panariello +3
Speaker verification systems are increasingly deployed in security-sensitive applications but remain highly vulnerable to adversarial perturbations. In this work, we propose the Ma…
Reference-free Adversarial Sex Obfuscation in Speech
Yangyang Qu, Michele Panariello, Massimiliano Todisco +1
Sex conversion in speech involves privacy risks from data collection and often leaves residual sex-specific cues in outputs, even when target speaker references are unavailable. We…
The Risks and Detection of Overestimated Privacy Protection in Voice Anonymisation
Michele Panariello, Sarina Meyer, Pierre Champion +4
Voice anonymisation aims to conceal the voice identity of speakers in speech recordings. Privacy protection is usually estimated from the difficulty of using a speaker verification…
Malacopula: adversarial automatic speaker verification attacks using a neural-based generalised Hammerstein model
Massimiliano Todisco, Michele Panariello, Xin Wang +3
We present Malacopula, a neural-based generalised Hammerstein model designed to introduce adversarial perturbations to spoofed speech utterances so that they better deceive automat…
Preserving spoken content in voice anonymisation with character-level vocoder conditioning
Michele Panariello, Massimiliano Todisco, Nicholas Evans
Voice anonymisation can be used to help protect speaker privacy when speech data is shared with untrusted others. In most practical applications, while the voice identity should be…
The VoicePrivacy 2022 Challenge: Progress and Perspectives in Voice Anonymisation
Michele Panariello, Natalia Tomashenko, Xin Wang +7
The VoicePrivacy Challenge promotes the development of voice anonymisation solutions for speech technology. In this paper we present a systematic overview and analysis of the secon…
Spoof Diarization: "What Spoofed When" in Partially Spoofed Audio
Lin Zhang, Xin Wang, Erica Cooper +4
This paper defines Spoof Diarization as a novel task in the Partial Spoof (PS) scenario. It aims to determine what spoofed when, which includes not only locating spoof regions but…
A Comparison of Differential Performance Metrics for the Evaluation of Automatic Speaker Verification Fairness
Oubaida Chouchane, Christoph Busch, Chiara Galdi +2
When decisions are made and when personal data is treated by automated processes, there is an expectation of fairness -- that members of different demographic groups receive equita…
The VoicePrivacy 2024 Challenge Evaluation Plan
Natalia Tomashenko, Xiaoxiao Miao, Pierre Champion +7
The task of the challenge is to develop a voice anonymization system for speech data which conceals the speaker's voice identity while protecting linguistic content and emotional s…
Speaker anonymization using neural audio codec language models
Michele Panariello, Francesco Nespoli, Massimiliano Todisco +1
The vast majority of approaches to speaker anonymization involve the extraction of fundamental frequency estimates, linguistic features and a speaker embedding which is perturbed t…