4 citations · 13 across the 31 of their papers we have counts for
11 papers · 1 filter
Kiwano: A Cutting-Edge Open-Source Toolkit for Speaker Verification
Mickael Rouvier, Pierre Michel Bousquet
In this paper, we present Kiwano, an open-source toolkit designed to advance research and evaluation for speaker verification. Kiwano provides a lightweight yet extensible framewor…
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…
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…
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…
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…
On Low-Bit Quantization Errors in Speaker Verification: Diagnostic and Mitigation
Hugo Leguillier, Driss Matrouf, Guillaume Lechien +1
Although low-bit quantization provides practical means to deploy speaker verification on resource-constrained devices, its effects on speaker verification performance remain poorly…