13 citations · 32 across the 40 of their papers we have counts for
43 papers
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…
An Empirical Analysis of Discrete Unit Representations in Speech Language Modeling Pre-training
Yanis Labrak, Richard Dufour, Mickaël Rouvier
This paper investigates discrete unit representations in Speech Language Models (SLMs), focusing on optimizing speech modeling during continual pre-training. In this paper, we syst…
LFAR: Accounting for Layerwise Dynamics to Improve Multimodal Adaptation of Language Models
Santiago Cuervo, Adel Moumen, Yanis Labrak +5
Text-pretrained language models (LMs) encode rich world knowledge, but adapting them to process and generate perceptual modalities such as audio and images while effectively levera…
A Benchmark of French ASR Systems Based on Error Severity
Antoine Tholly, Jane Wottawa, Mickael Rouvier +1
Automatic Speech Recognition (ASR) transcription errors are commonly assessed using metrics that compare them with a reference transcription, such as Word Error Rate (WER), which m…
MSP-Podcast SER Challenge 2024: L'antenne du Ventoux Multimodal Self-Supervised Learning for Speech Emotion Recognition
Jarod Duret, Mickael Rouvier, Yannick Estève
In this work, we detail our submission to the 2024 edition of the MSP-Podcast Speech Emotion Recognition (SER) Challenge. This challenge is divided into two distinct tasks: Categor…
Zero-Shot End-To-End Spoken Question Answering In Medical Domain
Yanis Labrak, Adel Moumen, Richard Dufour +1
In the rapidly evolving landscape of spoken question-answering (SQA), the integration of large language models (LLMs) has emerged as a transformative development. Conventional appr…
Asymmetric and trial-dependent modeling: the contribution of LIA to SdSV Challenge Task 2
Pierre-Michel Bousquet, Mickael Rouvier
The SdSv challenge Task 2 provided an opportunity to assess efficiency and robustness of modern text-independent speaker verification systems. But it also made it possible to test…