1 code implementation • 18 May 2020 • Xinchi Qiu, Titouan Parcollet, Mirco Ravanelli, Nicholas Lane, Mohamed Morchid
In this paper, we propose to capture these inter- and intra- structural dependencies with quaternion neural networks, which can jointly process multiple signals as whole quaternion entities.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 17 Jun 2019 • Titouan Parcollet, Mohamed Morchid, Georges Linarès, Renato de Mori
Deep neural networks (DNNs) and more precisely recurrent neural networks (RNNs) are at the core of modern automatic speech recognition systems, due to their efficiency to process input sequences.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 13 Apr 2019 • Titouan Parcollet, Mohamed Morchid, Xavier Bost, Georges Linarès
TRS transcripts are only used to measure the performances of ASR systems.
Generative Adversarial Network Spoken Language Understanding
no code implementations • 21 Nov 2018 • Titouan Parcollet, Mirco Ravanelli, Mohamed Morchid, Georges Linarès, Renato de Mori
Neural network architectures are at the core of powerful automatic speech recognition systems (ASR).
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
2 code implementations • 6 Nov 2018 • Titouan Parcollet, Mohamed Morchid, Georges Linarès, Renato de Mori
Recurrent neural networks (RNN) are at the core of modern automatic speech recognition (ASR) systems.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
1 code implementation • 20 Jun 2018 • Titouan Parcollet, Ying Zhang, Mohamed Morchid, Chiheb Trabelsi, Georges Linarès, Renato De Mori, Yoshua Bengio
Quaternion numbers and quaternion neural networks have shown their efficiency to process multidimensional inputs as entities, to encode internal dependencies, and to solve many tasks with less learning parameters than real-valued models.
Ranked #19 on Speech Recognition on TIMIT
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
3 code implementations • ICLR 2019 • Titouan Parcollet, Mirco Ravanelli, Mohamed Morchid, Georges Linarès, Chiheb Trabelsi, Renato de Mori, Yoshua Bengio
Recurrent neural networks (RNNs) are powerful architectures to model sequential data, due to their capability to learn short and long-term dependencies between the basic elements of a sequence.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 20 Mar 2017 • Mohamed Morchid, Juan-Manuel Torres-Moreno, Richard Dufour, Javier Ramírez-Rodríguez, Georges Linarès
One of the main difficulty in using topic model on huge data collection is related to the material resources (CPU time and memory) required for model estimate.
no code implementations • 21 Feb 2017 • Xavier Bost, Ilaria Brunetti, Luis Adrián Cabrera-Diego, Jean-Valère Cossu, Andréa Linhares, Mohamed Morchid, Juan-Manuel Torres-Moreno, Marc El-Bèze, Richard Dufour
The 2013 D\'efi de Fouille de Textes (DEFT) campaign is interested in two types of language analysis tasks, the document classification and the information extraction in the specialized domain of cuisine recipes.
no code implementations • 11 Feb 2017 • Mohamed Bouaziz, Mohamed Morchid, Richard Dufour, Georges Linarès, Renato de Mori
Nevertheless, these RNNs process a single input stream in one (LSTM) or two (Bidirectional LSTM) directions.
no code implementations • JEPTALNRECITAL 2016 • Mohamed Bouaziz, Mohamed Morchid, Richard Dufour, Georges Linar{\`e}s, Prosper Correa
Cet article pr{\'e}sente une m{\'e}thode de pr{\'e}diction de genres d{'}{\'e}missions t{\'e}l{\'e}vis{\'e}es couvrant 2 jours de diffusion de 4 cha{\^\i}nes TV fran{\c{c}}aises structur{\'e}s en {\'e}missions annot{\'e}es en genres.
no code implementations • JEPTALNRECITAL 2016 • Killian Janod, Mohamed Morchid, Richard Dufour, Georges Linar{\`e}s, Renato de Mori
Les repr{\'e}sentations de documents au moyen d{'}approches {\`a} base de r{\'e}seaux de neurones ont montr{\'e} des am{\'e}liorations significatives dans de nombreuses t{\^a}ches du traitement du langage naturel.
no code implementations • JEPTALNRECITAL 2016 • Mohamed Bouaziz, Mohamed Morchid, Pierre-Michel Bousquet, Richard Dufour, Killian Janod, Waad Ben Kheder, Georges Linar{\`e}s
Les applications de compr{\'e}hension du langage parl{\'e} sont moins performantes si les documents transcrits automatiquement contiennent un taux d{'}erreur-mot {\'e}lev{\'e}.
no code implementations • JEPTALNRECITAL 2015 • Carole Lailler, Yannick Est{\`e}ve, Renato de Mori, Mohamed Bouall{\`e}gue, Mohamed Morchid
Les travaux pr{\'e}sent{\'e}s portent sur l{'}extraction automatique d{'}unit{\'e}s s{\'e}mantiques et l{'}{\'e}valuation de leur pertinence pour des conversations t{\'e}l{\'e}phoniques.
no code implementations • JEPTALNRECITAL 2015 • Killian Janod, Mohamed Morchid, Richard Dufour, Georges Linares
Ces approches sont manipul{\'e}es au travers d{'}un r{\'e}seau de neurones, l{'}architecture CBOW cherchant alors {\`a} pr{\'e}dire un mot sachant son contexte, alors que l{'}architecture Skip-Gram pr{\'e}dit un contexte sachant un mot.
no code implementations • JEPTALNRECITAL 2015 • Mohamed Morchid, Richard Dufour, Georges Linar{\`e}s
La m{\'e}thode propos{\'e}e consiste {\`a} configurer la topologie d{'}un ANN ainsi que d{'}initialiser les connexions de celui-ci {\`a} l{'}aide des espaces th{\'e}matiques appris pr{\'e}c{\'e}demment.
no code implementations • LREC 2014 • Mohamed Morchid, Georges Linar{\`e}s, Richard Dufour
The prediction of bursty events on the Internet is a challenging task.
no code implementations • LREC 2014 • Mohamed Morchid, Richard Dufour, Georges Linar{\`e}s
Although the current transcription systems could achieve high recognition performance, they still have a lot of difficulties to transcribe speech in very noisy environments.