activity
20212026
most citedVocoder drift compensation by x-vector alignment in speaker anonymisation

1 citations · 2 across the 15 of their papers we have counts for

collaborators
Showing eess.ASShow all

17 papers · 1 filter

eess.AS2026

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…

eess.AS2026

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…

eess.AS2026

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…

eess.AS2026

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…

eess.AS2025

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…

eess.AS2025

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…