13 citations · 32 across the 40 of their papers we have counts for
18 papers · 1 filter
Entropy-aware logistic regression for fusion of large-scale speaker recognition systems
Pierre-Michel Bousquet, Mickael Rouvier
Score-level fusion based on logistic regression is widely used in speaker recognition to combine complementary systems. However, conventional approaches assign fixed system-depende…
Beyond Encoder Fusion: Multi-View Discrete Token Augmentation for LLM-Based ASR
Paul Moïse Gangbadja, Mickael Rouvier, Fabrice Lefèvre
Discrete speech tokens provide a compact interface between speech encoders and large language models for automatic speech recognition, but single-tokenization systems remain sensit…
Generative vs. Encoder Large Language Models for ASR Evaluation: A Comparative Study
Thibault Bañeras-Roux, Shashi Kumar, Driss Khalil +6
Automatic Speech Recognition (ASR) is typically evaluated using Word Error Rate (WER), which poorly reflects semantic similarity. While embedding-based metrics correlate better wit…
Streaming Neural Speech Codecs through Time-Invariant Representations
Kélian Estève, Salima Mhdaffar, Mickael Rouvier +2
Neural speech codecs are increasingly used as intermediate representations in codec-based speech generation systems. TiCodec introduces a factorized representation that separates t…
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…
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…
A Large-Scale Per-Speaker Analysis of Re-identification Risk in Speech Anonymization
Orane Dufour, Paul Magron, Mickael Rouvier +1
Speech anonymization is commonly evaluated using averagecase metrics such as the equal error rate, which can hide large disparities in re-identification risks across individuals. I…
Evaluation of Automatic Speech Recognition Using Generative Large Language Models
Thibault Bañeras-Roux, Shashi Kumar, Driss Khalil +6
Automatic Speech Recognition (ASR) is traditionally evaluated using Word Error Rate (WER), a metric that is insensitive to meaning. Embedding-based semantic metrics are better corr…
A Comprehensive Analysis of Tokenization and Self-Supervised Learning in End-to-End Automatic Speech Recognition applied on French Language
Thibault Bañeras-Roux, Mickael Rouvier, Jane Wottawa +1
The performance of end-to-end automatic speech recognition (ASR) systems enables their increasing integration into numerous applications. While there are various benefits to such s…
A Paradigm for Interpreting Metrics and Identifying Critical Errors in Automatic Speech Recognition
Thibault Bañeras-Roux, Mickael Rouvier, Jane Wottawa +1
The most commonly used metrics for evaluating automatic speech transcriptions, namely Word Error Rate (WER) and Character Error Rate (CER), have been heavily criticized for their p…
HATS: An Open data set Integrating Human Perception Applied to the Evaluation of Automatic Speech Recognition Metrics
Thibault Bañeras Roux, Jane Wottawa, Mickael Rouvier +2
Conventionally, Automatic Speech Recognition (ASR) systems are evaluated on their ability to correctly recognize each word contained in a speech signal. In this context, the word e…
Qualitative Evaluation of Language Model Rescoring in Automatic Speech Recognition
Thibault Bañeras-Roux, Mickaël Rouvier, Jane Wottawa +1
Evaluating automatic speech recognition (ASR) systems is a classical but difficult and still open problem, which often boils down to focusing only on the word error rate (WER). How…
Enhancing Multi-Corpus Training in SSL-Based Anti-Spoofing Models: Domain-Invariant Feature Extraction
Anh-Tuan Dao, Driss Matrouf, Mickael Rouvier +1
The performance of speech spoofing detection often varies across different training and evaluation corpora. Leveraging multiple corpora typically enhances robustness and performanc…