Zobrazeno 1 - 10
of 114
pro vyhledávání: '"Yannick Estève"'
Publikováno v:
Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki, Vol 16, Iss 6, Pp 1063-1072 (2016)
Subject of Research. We study speaker adaptation of deep neural network (DNN) acoustic models in automatic speech recognition systems. The aim of speaker adaptation techniques is to improve the accuracy of the speech recognition system for a particul
Externí odkaz:
https://doaj.org/article/bf005eb246874a83acffa1b6722b35c8
This paper presents a study on the use of federated learning to train an ASR model based on a wav2vec 2.0 model pre-trained by self supervision. Carried out on the well-known TED-LIUM 3 dataset, our experiments show that such a model can obtain, with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1e328d3a34dbbb67e0451b9427824bfe
Publikováno v:
JEP 2022
JEP 2022, Jun 2022, île de Noirmoutier, France
JEP 2022, Jun 2022, île de Noirmoutier, France
National audience; Plusieurs services intégrés dans notre vie quotidienne utilisent la reconnaissance automatique de la parole (Apple-Siri, Amazon-Alexa...). Ces services s'appuient sur des modèles entraînés sur une grande quantité de données
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::488074dda5e21679943deabde2e9e10f
http://hdl.handle.net/20.500.12210/80092
http://hdl.handle.net/20.500.12210/80092
Le benchmark MEDIA revisité : données, outils et évaluation dans un contexte d’apprentissage profond
Autor:
Gaëlle Laperrière, Valentin Pelloin, Antoine Caubrière, Salima Mdhaffar, Nathalie Camelin, Sahar Ghannay, Bassam Jabaian, Yannick Estève
Publikováno v:
XXXIVe Journées d'Études sur la Parole -- JEP 2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a462bbbb9637fe8f589d1943942291da
https://doi.org/10.21437/jep.2022-51
https://doi.org/10.21437/jep.2022-51
Publikováno v:
XXXIVe Journées d'Études sur la Parole -- JEP 2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::105da95cb67888fd42f508909fa21df9
https://doi.org/10.21437/jep.2022-91
https://doi.org/10.21437/jep.2022-91
Self-supervised models for speech processing emerged recently as popular foundation blocks in speech processing pipelines. These models are pre-trained on unlabeled audio data and then used in speech processing downstream tasks such as automatic spee
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8401d011e50eb17e736d1c2b6939953c
http://arxiv.org/abs/2204.01397
http://arxiv.org/abs/2204.01397
Publikováno v:
Speech Communication
Speech Communication, Elsevier : North-Holland, 2020
Speech Communication, Elsevier : North-Holland, 2020
International audience; This paper presents a study of continuous word representations applied to automatic detection of speech recognition errors. A neural network architecture is proposed, which is well suited to handle continuous word representati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::01ceadc2d8a585db956ed3684db37275
https://doi.org/10.1016/j.specom.2020.03.002
https://doi.org/10.1016/j.specom.2020.03.002
Autor:
Hang Le, Sina Alisamir, Marco Dinarelli, Fabien Ringeval, Solène Evain, Ha Nguyen, Marcely Zanon Boito, Salima Mdhaffar, Ziyi Tong, Natalia Tomashenko, Titouan Parcollet, Alexandre Allauzen, Yannick Estève, Benjamin Lecouteux, François Portet, Solange Rossato, Didier Schwab, Laurent Besacier
Publikováno v:
HAL
L'apprentissage autosupervisé a apporté des améliorations remarquables dans de nombreux domaines tels que la vision par ordinateur ou le traitement de la langue et de la parole, en exploitant de grandes quantités de données non étiquetées. Dan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5b45625171543c36fa01b1db28207288
https://hal.archives-ouvertes.fr/hal-03706952
https://hal.archives-ouvertes.fr/hal-03706952
Publikováno v:
Expert Systems with Applications. 213:119238
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c6e5239a4091babaa17494aadf97504c
https://doi.org/10.1016/j.eswa.2022.119238
https://doi.org/10.1016/j.eswa.2022.119238
The goal of our research is to automaticaly retrieve the satisfaction and the frustration in real-life call-center conversations. This study focuses an industrial application in which the customer satisfaction is continuously tracked down to improve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::89d7f276cca29926c910baa3851c0776
https://doi.org/10.36227/techrxiv.17104526
https://doi.org/10.36227/techrxiv.17104526