no code implementations • LREC 2022 • Thibault Prouteau, Nicolas Dugué, Nathalie Camelin, Sylvain Meignier
It is operated using a large French corpus, and is thus, as far as we know, the first large-scale experiment regarding word embedding interpretability on this language.
no code implementations • LREC 2022 • Salima Mdhaffar, Valentin Pelloin, Antoine Caubrière, Gaëlle Laperriere, Sahar Ghannay, Bassam Jabaian, Nathalie Camelin, Yannick Estève
Pretrained models through self-supervised learning have been recently introduced for both acoustic and language modeling.
no code implementations • LREC 2022 • Gaëlle Laperrière, Valentin Pelloin, Antoine Caubrière, Salima Mdhaffar, Nathalie Camelin, Sahar Ghannay, Bassam Jabaian, Yannick Estève
In this paper, we focus on the French MEDIA SLU dataset, distributed since 2005 and used as a benchmark dataset for a large number of research works.
no code implementations • 5 Jul 2022 • Valentin Pelloin, Franck Dary, Nicolas Herve, Benoit Favre, Nathalie Camelin, Antoine Laurent, Laurent Besacier
We aim at improving spoken language modeling (LM) using very large amount of automatically transcribed speech.
no code implementations • 1 Feb 2021 • Valentin Pelloin, Nathalie Camelin, Antoine Laurent, Renato de Mori, Antoine Caubrière, Yannick Estève, Sylvain Meignier
In this paper, we propose a novel end-to-end sequence-to-sequence spoken language understanding model using an attention mechanism.
no code implementations • LREC 2020 • Amira Barhoumi, Nathalie Camelin, Chafik Aloulou, Yannick Est{\`e}ve, lamia hadrich belguith
This work presents a study that compares embeddings based on words and lemmas in SA frame.
no code implementations • LREC 2020 • Salima Mdhaffar, Yannick Est{\`e}ve, Antoine Laurent, Hern, Nicolas ez, Richard Dufour, Delphine Charlet, Geraldine Damnati, Solen Quiniou, Nathalie Camelin
The use cases concern scientific fields from both speech and text processing, with language model adaptation, thematic segmentation and transcription to slide alignment.
no code implementations • JEPTALNRECITAL 2019 • Amira Barhoumi, Nathalie Camelin, Chafik Aloulou, Yannick Est{\`e}ve, lamia hadrich belguith
Nous nous int{\'e}ressons, dans cet article, {\`a} la t{\^a}che d{'}analyse d{'}opinions en arabe.
no code implementations • 18 Jun 2019 • Antoine Caubrière, Natalia Tomashenko, Antoine Laurent, Emmanuel Morin, Nathalie Camelin, Yannick Estève
We present an end-to-end approach to extract semantic concepts directly from the speech audio signal.
no code implementations • JEPTALNRECITAL 2018 • Amira Barhoumi, Nathalie Camelin, Yannick Est{\`e}ve
Ces derni{\`e}res ann{\'e}es, l{'}utilisation de l{'}apprentissage profond a am{\'e}lior{\'e} des performances de nombreux syst{\`e}mes automatiques dans une grande vari{\'e}t{\'e} de domaines (analyse d{'}images, reconnaissance de la parole, traduction automatique, .
no code implementations • 26 May 2017 • Edwin Simonnet, Sahar Ghannay, Nathalie Camelin, Yannick Estève, Renato de Mori
This paper addresses the problem of automatic speech recognition (ASR) error detection and their use for improving spoken language understanding (SLU) systems.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
no code implementations • JEPTALNRECITAL 2016 • Sahar Ghannay, Yannick Est{\`e}ve, Nathalie Camelin, Camille Dutrey, Fabian Santiago, Martine Adda-Decker
Dans cet article, nous proposons d{'}{\'e}tudier leur utilisation dans une architecture neuronale pour la t{\^a}che de d{\'e}tection des erreurs au sein de transcriptions automatiques de la parole.
no code implementations • JEPTALNRECITAL 2016 • Edwin Simonnet, Paul Del{\'e}glise, Nathalie Camelin, Yannick Est{\`e}ve
L{'}{\'e}tude porte sur l{'}apport d{'}un r{\'e}seau de neurones r{\'e}current (Recurrent Neural Network RNN) bidirectionnel encodeur/d{\'e}codeur avec m{\'e}canisme d{'}attention pour une t{\^a}che de compr{\'e}hension de la parole.
no code implementations • JEPTALNRECITAL 2016 • Olivier Galibert, Nathalie Camelin, Paul Del{\'e}glise, Sophie Rosset
Nous comparons ici diff{\'e}rentes m{\'e}triques, notamment le WER, NE-WER et ATENE m{\'e}trique propos{\'e}e r{\'e}cemment pour l{'}{\'e}valuation des syst{\`e}mes de reconnaissance de la parole {\'e}tant donn{\'e} une t{\^a}che de reconnaissance d{'}entit{\'e}s nomm{\'e}es.
no code implementations • LREC 2016 • Sahar Ghannay, Benoit Favre, Yannick Est{\`e}ve, Nathalie Camelin
Different approaches have been introduced to calculate word embeddings through neural networks.
no code implementations • JEPTALNRECITAL 2015 • Abdessalam Bouchekif, G{\'e}raldine Damnati, Nathalie Camelin, Yannick Est{\`e}ve, Delphine Charlet
Dans cet article, nous nous int{\'e}ressons au titrage automatique des segments issus de la segmentation th{\'e}matique de journaux t{\'e}l{\'e}vis{\'e}s. Nous proposons d{'}associer un segment {\`a} un article de presse {\'e}crite collect{\'e} le jour m{\^e}me de la diffusion du journal.
no code implementations • LREC 2014 • Daniel Luzzati, Cyril Grouin, Ioana Vasilescu, Martine Adda-Decker, Eric Bilinski, Nathalie Camelin, Juliette Kahn, Carole Lailler, Lori Lamel, Sophie Rosset
This paper is concerned with human assessments of the severity of errors in ASR outputs.
no code implementations • JEPTALNRECITAL 2012 • Fabrice Lef{\`e}vre, Djamel Mostefa, Laurent Besacier, Yannick Est{\`e}ve, Matthieu Quignard, Nathalie Camelin, Benoit Favre, Bassam Jabaian, Lina Rojas-Barahona
no code implementations • LREC 2012 • Fabrice Lef{\`e}vre, Djamel Mostefa, Laurent Besacier, Yannick Est{\`e}ve, Matthieu Quignard, Nathalie Camelin, Benoit Favre, Bassam Jabaian, Lina M. Rojas-Barahona
The PORTMEDIA project is intended to develop new corpora for the evaluation of spoken language understanding systems.