Search Results for author: Renato de Mori

Found 15 papers, 5 papers with code

End2End Acoustic to Semantic Transduction

no code implementations1 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.

Language Modelling Spoken Language Understanding

Dialogue history integration into end-to-end signal-to-concept spoken language understanding systems

no code implementations14 Feb 2020 Natalia Tomashenko, Christian Raymond, Antoine Caubriere, Renato de Mori, Yannick Esteve

The dialog history is represented in the form of dialog history embedding vectors (so-called h-vectors) and is provided as an additional information to end-to-end SLU models in order to improve the system performance.

Slot Filling Spoken Language Understanding

Real to H-space Encoder for Speech Recognition

no code implementations17 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 Frame

Multiple topic identification in telephone conversations

no code implementations21 Dec 2018 Xavier Bost, Marc El Bèze, Renato de Mori

Furthermore, using the theme skeleton of a conversation from which thematic densities are derived, it will be possible to extract components of an automatic conversation report to be used for improving the service performance.

Document Classification

Speech recognition with quaternion neural networks

1 code implementation21 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

Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition

1 code implementation20 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.

Automatic Speech Recognition Frame

Quaternion Recurrent Neural Networks

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

ASR error management for improving spoken language understanding

no code implementations26 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 Spoken Language Understanding +1

Auto-encodeurs pour la compr\'ehension de documents parl\'es (Auto-encoders for Spoken Document Understanding)

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.

Utilisation d'annotations s\'emantiques pour la validation automatique d'hypoth\`eses dans des conversations t\'el\'ephoniques

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.

DECODA: a call-centre human-human spoken conversation corpus

no code implementations LREC 2012 Frederic Bechet, Benjamin Maza, Nicolas Bigouroux, Thierry Bazillon, Marc El-B{\`e}ze, Renato de Mori, Eric Arbillot

The goal of the DECODA project is to reduce the development cost of Speech Analytics systems by reducing the need for manual annotat ion.

Speech Recognition

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