collaborators

10 papers

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…

cs.CL2026

Linguistic Bias Mitigation for Spoofing Detection via Gradient Reversal and A Variational Information Bottleneck

Anh-Tuan Dao, Driss Matrouf, Mickael Rouvier +1

Rapid advancements in generative speech technology have compromised the reliability of voice biometrics. While current spoofing detectors excel when assessed under in-domain condit…

cs.SD2026

From Self-Supervised Speech Models to Mixture-of-Experts for Robust Anti-Spoofing

Hugo Daumain, Driss Matrouf, Khaled Khelif +1

Recent advances in speech generation have significantly improved the naturalness of synthetic speech, making spoofing detection increasingly challenging. A key limitation of curren…

cs.SD2026

Speaker-Invariant Representation Learning for Spoofing Detection via Gradient Reversal and A Variational Information Bottleneck

Anh-Tuan Dao, Driss Matrouf, Mickael Rouvier +1

Sophisticated generative speech technology can undermined the reliability of voice biometrics. While spoofing detection systems excel when assessed under in-domain conditions, gene…

cs.SD2026

A Comparison of SSL-Based Feature Extractors and Back-End Classifiers for Spoofing Detection: A Multi-Corpus Training and Cross-Linguistic Analysis

Anh-Tuan Dao, Driss Matrouf, Mickael Rouvier +1

Voice biometric systems face growing threats from spoofing attacks, yet the evaluation of detection models remains inconsistent across datasets. To investigate these unpredictable…

cs.SD2026

Assessing the Energy and Carbon Emissions of Neural Speaker Verification Model in Training and Inference

Hugo Leguillier, Driss Matrouf, Guillaume Lechien +1

Deep-learning speaker verification (SV) increasingly relies on deep neural network backbones, whose environmental impact remains largely undocumented. In this paper, we conduct an…