2 citations · 5 across the 21 of their papers we have counts for
25 papers
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…
Multi-sample Synthetic Supervision for Accent Conversion
Yangyang Qu, Michele Panariello, Massimiliano Todisco +1
Accent conversion (AC) requires changing accent while preserving speaker identity and linguistic content, yet parallel recordings are scarce. Speech synthesis provides an alternati…
Shared-State Local Translations for Training-Free Voice Conversion
Yangyang Qu, Michele Panariello, Massimiliano Todisco +1
In one-shot training-free voice conversion (VC), the source and reference utterances may contain different linguistic content, so reliable frame-level correspondence between them c…
Partial Accent-Control Editing in Frozen Speech Representations for Accent Conversion
Yangyang Qu, Michele Panariello, Massimiliano Todisco +1
Accent conversion is the task of modifying a speech recording so that it sounds closer to a target accent while preserving linguistic content and other speaker-related characterist…
Why Do You Say It Like That? A Phoneme-level Framework for Explainable Speech Deepfake Detection
Anna Taylor, Michele Panariello, Massimiliano Todisco +3
As the accuracy of speech deepfake detection improves with the use of self-supervised representations such as wav2vec 2.0 and HuBERT, understanding why the speech is classified as…
Positive-Incentive Noise Predictor for Adversarial Purification in Speaker Verification
Yibo Bai, Sizhou Chen, Michele Panariello +5
Modern automatic speaker verification (ASV) systems are vulnerable to adversarial perturbations. Diffusion-based purification has recently shown strong effectiveness against such p…
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…
Identity leakage through accent cues in voice anonymisation
Rayane Bakari, Olivier Le Blouch, Nicolas Gengembre +2
Voice anonymisation is used to conceal voice identity while preserving linguistic content. Even if anonymisation seems strong, non-timbral cues such as accent that remain post-anon…
The Third VoicePrivacy Challenge: Preserving Emotional Expressiveness and Linguistic Content in Voice Anonymization
Natalia Tomashenko, Xiaoxiao Miao, Pierre Champion +7
We present results and analyses from the third VoicePrivacy Challenge held in 2024, which focuses on advancing voice anonymization technologies. The task was to develop a voice ano…
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…
2D-Malafide: Adversarial Attacks Against Face Deepfake Detection Systems
Chiara Galdi, Michele Panariello, Massimiliano Todisco +1
We introduce 2D-Malafide, a novel and lightweight adversarial attack designed to deceive face deepfake detection systems. Building upon the concept of 1D convolutional perturbation…
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…
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…
Fairness and Privacy in Voice Biometrics:A Study of Gender Influences Using wav2vec 2.0
Oubaida Chouchane, Michele Panariello, Chiara Galdi +2
This study investigates the impact of gender information on utility, privacy, and fairness in voice biometric systems, guided by the General Data Protection Regulation (GDPR) manda…
Vocoder drift compensation by x-vector alignment in speaker anonymisation
Michele Panariello, Massimiliano Todisco, Nicholas Evans
For the most popular x-vector-based approaches to speaker anonymisation, the bulk of the anonymisation can stem from vocoding rather than from the core anonymisation function which…
Differentially Private Adversarial Auto-Encoder to Protect Gender in Voice Biometrics
Oubaïda Chouchane, Michele Panariello, Oualid Zari +4
Over the last decade, the use of Automatic Speaker Verification (ASV) systems has become increasingly widespread in response to the growing need for secure and efficient identity v…
Malafide: a novel adversarial convolutive noise attack against deepfake and spoofing detection systems
Michele Panariello, Wanying Ge, Hemlata Tak +2
We present Malafide, a universal adversarial attack against automatic speaker verification (ASV) spoofing countermeasures (CMs). By introducing convolutional noise using an optimis…
Vocoder drift in x-vector-based speaker anonymization
Michele Panariello, Massimiliano Todisco, Nicholas Evans
State-of-the-art approaches to speaker anonymization typically employ some form of perturbation function to conceal speaker information contained within an x-vector embedding, then…