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20242026
most citedProbing the Information Encoded in Neural-based Acoustic Models of Automatic Speech Recognition Systems

2 citations · 3 across the 12 of their papers we have counts for

collaborators

14 papers

cs.CL2026

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…

cs.CL2026

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…

cs.CL2026

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…

cs.CL2026★ 1 cited

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…

cs.CL2026

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…

cs.CL2026

MedInjection-FR: Exploring the Role of Native, Synthetic, and Translated Data in Biomedical Instruction Tuning

Ikram Belmadani, Oumaima El Khettari, Pacôme Constant dit Beaufils +2

Instruction tuning has become essential for adapting large language models (LLMs) to follow domain-specific prompts. Yet, in specialized fields such as medicine, the scarcity of hi…

cs.CL2025

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…

cs.CL2025

Improving Social Determinants of Health Documentation in French EHRs Using Large Language Models

Adrien Bazoge, Pacôme Constant dit Beaufils, Mohammed Hmitouch +6

Social determinants of health (SDoH) significantly influence health outcomes, shaping disease progression, treatment adherence, and health disparities. However, their documentation…

cs.CL2025

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…

cs.CL2024

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…

cs.SD2024★ 2 cited

Probing the Information Encoded in Neural-based Acoustic Models of Automatic Speech Recognition Systems

Quentin Raymondaud, Mickael Rouvier, Richard Dufour

Deep learning architectures have made significant progress in terms of performance in many research areas. The automatic speech recognition (ASR) field has thus benefited from thes…

cs.CL2024

DrBenchmark: A Large Language Understanding Evaluation Benchmark for French Biomedical Domain

Yanis Labrak, Adrien Bazoge, Oumaima El Khettari +8

The biomedical domain has sparked a significant interest in the field of Natural Language Processing (NLP), which has seen substantial advancements with pre-trained language models…

cs.CL2024

How Important Is Tokenization in French Medical Masked Language Models?

Yanis Labrak, Adrien Bazoge, Beatrice Daille +2

Subword tokenization has become the prevailing standard in the field of natural language processing (NLP) over recent years, primarily due to the widespread utilization of pre-trai…

cs.CL2024

BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains

Yanis Labrak, Adrien Bazoge, Emmanuel Morin +3

Large Language Models (LLMs) have demonstrated remarkable versatility in recent years, offering potential applications across specialized domains such as healthcare and medicine. D…