no code implementations • JEP/TALN/RECITAL 2022 • Yanis Labrak, Philippe Turcotte, Richard Dufour, Mickael Rouvier
Nous proposons trois systèmes de classification reposant sur des caractéristiques extraites de plongements de mots contextuels issus d’un modèle BERT (CamemBERT).
no code implementations • LREC 2022 • Mickael Rouvier, Mohammad Mohammadamini
The main goal of this corpus is to foster research in far-field and multi-channel text-independent speaker verification.
no code implementations • 18 Jan 2025 • Antoine Tholly, Jane Wottawa, Mickael Rouvier, Richard Dufour
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 measures spelling deviations from the reference, or semantic score-based metrics.
no code implementations • 8 Jul 2024 • 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.
no code implementations • 29 Jun 2024 • Mirco Ravanelli, Titouan Parcollet, Adel Moumen, Sylvain de Langen, Cem Subakan, Peter Plantinga, Yingzhi Wang, Pooneh Mousavi, Luca Della Libera, Artem Ploujnikov, Francesco Paissan, Davide Borra, Salah Zaiem, Zeyu Zhao, Shucong Zhang, Georgios Karakasidis, Sung-Lin Yeh, Pierre Champion, Aku Rouhe, Rudolf Braun, Florian Mai, Juan Zuluaga-Gomez, Seyed Mahed Mousavi, Andreas Nautsch, Xuechen Liu, Sangeet Sagar, Jarod Duret, Salima Mdhaffar, Gaelle Laperriere, Mickael Rouvier, Renato de Mori, Yannick Esteve
This paper presents SpeechBrain 1. 0, a significant milestone in the evolution of the toolkit, which now has over 200 recipes for speech, audio, and language processing tasks, and more than 100 models available on Hugging Face.
no code implementations • 9 Jun 2024 • Yanis Labrak, Adel Moumen, Richard Dufour, Mickael Rouvier
In the rapidly evolving landscape of spoken question-answering (SQA), the integration of large language models (LLMs) has emerged as a transformative development.
no code implementations • 28 Mar 2024 • 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.
no code implementations • 29 Feb 2024 • Quentin Raymondaud, Mickael Rouvier, Richard Dufour
Following many researches in neural networks interpretability, we propose in this article a protocol that aims to determine which and where information is located in an ASR acoustic model (AM).
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 22 Feb 2024 • Yanis Labrak, Adrien Bazoge, Beatrice Daille, Mickael Rouvier, Richard Dufour
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-trained language models.
1 code implementation • 20 Feb 2024 • Yanis Labrak, Adrien Bazoge, Oumaima El Khettari, Mickael Rouvier, Pacome Constant dit Beaufils, Natalia Grabar, Beatrice Daille, Solen Quiniou, Emmanuel Morin, Pierre-Antoine Gourraud, Richard Dufour
This limitation hampers the evaluation of the latest French biomedical models, as they are either assessed on a minimal number of tasks with non-standardized protocols or evaluated using general downstream tasks.
1 code implementation • 15 Feb 2024 • Yanis Labrak, Adrien Bazoge, Emmanuel Morin, Pierre-Antoine Gourraud, Mickael Rouvier, Richard Dufour
This marks the first large-scale multilingual evaluation of LLMs in the medical domain.
Ranked #8 on Few-Shot Learning on MedConceptsQA
no code implementations • 11 Sep 2023 • Titouan Parcollet, Ha Nguyen, Solene Evain, Marcely Zanon Boito, Adrien Pupier, Salima Mdhaffar, Hang Le, Sina Alisamir, Natalia Tomashenko, Marco Dinarelli, Shucong Zhang, Alexandre Allauzen, Maximin Coavoux, Yannick Esteve, Mickael Rouvier, Jerome Goulian, Benjamin Lecouteux, Francois Portet, Solange Rossato, Fabien Ringeval, Didier Schwab, Laurent Besacier
Self-supervised learning (SSL) is at the origin of unprecedented improvements in many different domains including computer vision and natural language processing.
no code implementations • 22 Jul 2023 • Yanis Labrak, Mickael Rouvier, Richard Dufour
We evaluate four state-of-the-art instruction-tuned large language models (LLMs) -- ChatGPT, Flan-T5 UL2, Tk-Instruct, and Alpaca -- on a set of 13 real-world clinical and biomedical natural language processing (NLP) tasks in English, such as named-entity recognition (NER), question-answering (QA), relation extraction (RE), etc.
1 code implementation • LOUHI 2022 • Yanis Labrak, Adrien Bazoge, Richard Dufour, Mickael Rouvier, Emmanuel Morin, Béatrice Daille, Pierre-Antoine Gourraud
This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain.
no code implementations • 3 Apr 2023 • Yanis Labrak, Adrien Bazoge, Richard Dufour, Mickael Rouvier, Emmanuel Morin, Béatrice Daille, Pierre-Antoine Gourraud
In recent years, pre-trained language models (PLMs) achieve the best performance on a wide range of natural language processing (NLP) tasks.
no code implementations • 2 Nov 2022 • Kong Aik Lee, Tomi Kinnunen, Daniele Colibro, Claudio Vair, Andreas Nautsch, Hanwu Sun, Liang He, Tianyu Liang, Qiongqiong Wang, Mickael Rouvier, Pierre-Michel Bousquet, Rohan Kumar Das, Ignacio Viñals Bailo, Meng Liu, Héctor Deldago, Xuechen Liu, Md Sahidullah, Sandro Cumani, Boning Zhang, Koji Okabe, Hitoshi Yamamoto, Ruijie Tao, Haizhou Li, Alfonso Ortega Giménez, Longbiao Wang, Luis Buera
This manuscript describes the I4U submission to the 2020 NIST Speaker Recognition Evaluation (SRE'20) Conversational Telephone Speech (CTS) Challenge.
1 code implementation • LREC 2022 • Marcely Zanon Boito, Fethi Bougares, Florentin Barbier, Souhir Gahbiche, Loïc Barrault, Mickael Rouvier, Yannick Estève
In this paper we present two datasets for Tamasheq, a developing language mainly spoken in Mali and Niger.
no code implementations • 13 Sep 2021 • Mickael Rouvier, Pierre-Michel Bousquet
A channel attention mechanism, called squeeze-and-excitation (SE), has recently been proposed in convolution layers and applied to speaker verification.
no code implementations • 10 May 2021 • Mickael Rouvier, Pierre-Michel Bousquet, Jarod Duret
The x-vector architecture has recently achieved state-of-the-art results on the speaker verification task.
no code implementations • EMNLP (IWSLT) 2019 • Ha Nguyen, Natalia Tomashenko, Marcely Zanon Boito, Antoine Caubriere, Fethi Bougares, Mickael Rouvier, Laurent Besacier, Yannick Esteve
This paper describes the ON-TRAC Consortium translation systems developed for the end-to-end model task of IWSLT Evaluation 2019 for the English-to-Portuguese language pair.
no code implementations • 16 Apr 2019 • Kong Aik Lee, Ville Hautamaki, Tomi Kinnunen, Hitoshi Yamamoto, Koji Okabe, Ville Vestman, Jing Huang, Guohong Ding, Hanwu Sun, Anthony Larcher, Rohan Kumar Das, Haizhou Li, Mickael Rouvier, Pierre-Michel Bousquet, Wei Rao, Qing Wang, Chunlei Zhang, Fahimeh Bahmaninezhad, Hector Delgado, Jose Patino, Qiongqiong Wang, Ling Guo, Takafumi Koshinaka, Jiacen Zhang, Koichi Shinoda, Trung Ngo Trong, Md Sahidullah, Fan Lu, Yun Tang, Ming Tu, Kah Kuan Teh, Huy Dat Tran, Kuruvachan K. George, Ivan Kukanov, Florent Desnous, Jichen Yang, Emre Yilmaz, Longting Xu, Jean-Francois Bonastre, Cheng-Lin Xu, Zhi Hao Lim, Eng Siong Chng, Shivesh Ranjan, John H. L. Hansen, Massimiliano Todisco, Nicholas Evans
The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE).
no code implementations • SEMEVAL 2017 • Mickael Rouvier
This paper describes the system developed at LIA for the SemEval-2017 evaluation campaign.
no code implementations • 15 Dec 2016 • Mickael Rouvier, Benoit Favre
Creating sentiment polarity lexicons is labor intensive.
no code implementations • JEPTALNRECITAL 2016 • Sebastien Delecraz, Frederic Bechet, Benoit Favre, Mickael Rouvier
L{'}identification du r{\^o}le d{'}un locuteur dans des {\'e}missions de t{\'e}l{\'e}vision est un probl{\`e}me de classification de personne selon une liste de r{\^o}les comme pr{\'e}sentateur, journaliste, invit{\'e}, etc.