Search Results for author: Yannick Estève

Found 15 papers, 2 papers with code

Retrieving Speaker Information from Personalized Acoustic Models for Speech Recognition

no code implementations7 Nov 2021 Salima Mdhaffar, Jean-François Bonastre, Marc Tommasi, Natalia Tomashenko, Yannick Estève

The widespread of powerful personal devices capable of collecting voice of their users has opened the opportunity to build speaker adapted speech recognition system (ASR) or to participate to collaborative learning of ASR.

Speaker Verification Speech Recognition

Privacy attacks for automatic speech recognition acoustic models in a federated learning framework

no code implementations6 Nov 2021 Natalia Tomashenko, Salima Mdhaffar, Marc Tommasi, Yannick Estève, Jean-François Bonastre

This paper investigates methods to effectively retrieve speaker information from the personalized speaker adapted neural network acoustic models (AMs) in automatic speech recognition (ASR).

automatic-speech-recognition Federated Learning +1

Where are we in semantic concept extraction for Spoken Language Understanding?

no code implementations24 Jun 2021 Sahar Ghannay, Antoine Caubrière, Salima Mdhaffar, Gaëlle Laperrière, Bassam Jabaian, Yannick Estève

More recent works on self-supervised training with unlabeled data open new perspectives in term of performance for automatic speech recognition and natural language processing.

automatic-speech-recognition Language understanding +4

Impact of Encoding and Segmentation Strategies on End-to-End Simultaneous Speech Translation

no code implementations29 Apr 2021 Ha Nguyen, Yannick Estève, Laurent Besacier

Boosted by the simultaneous translation shared task at IWSLT 2020, promising end-to-end online speech translation approaches were recently proposed.

Translation

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 Language understanding +1

On the use of Self-supervised Pre-trained Acoustic and Linguistic Features for Continuous Speech Emotion Recognition

no code implementations18 Nov 2020 Manon Macary, Marie Tahon, Yannick Estève, Anthony Rousseau

Pre-training for feature extraction is an increasingly studied approach to get better continuous representations of audio and text content.

Speech Emotion Recognition

Leverage Unlabeled Data for Abstractive Speech Summarization with Self-Supervised Learning and Back-Summarization

no code implementations30 Jul 2020 Paul Tardy, Louis de Seynes, François Hernandez, Vincent Nguyen, David Janiszek, Yannick Estève

In order to build a corpus for this task, it is necessary to obtain the (automatic or manual) transcription of each meeting, and then to segment and align it with the corresponding manual report to produce training examples suitable for training.

Abstractive Text Summarization Denoising +2

Align then Summarize: Automatic Alignment Methods for Summarization Corpus Creation

2 code implementations LREC 2020 Paul Tardy, David Janiszek, Yannick Estève, Vincent Nguyen

We report automatic alignment and summarization performances on this corpus and show that automatic alignment is relevant for data annotation since it leads to large improvement of almost +4 on all ROUGE scores on the summarization task.

Meeting Summarization Text Summarization

ON-TRAC Consortium for End-to-End and Simultaneous Speech Translation Challenge Tasks at IWSLT 2020

no code implementations WS 2020 Maha Elbayad, Ha Nguyen, Fethi Bougares, Natalia Tomashenko, Antoine Caubrière, Benjamin Lecouteux, Yannick Estève, Laurent Besacier

This paper describes the ON-TRAC Consortium translation systems developed for two challenge tracks featured in the Evaluation Campaign of IWSLT 2020, offline speech translation and simultaneous speech translation.

Data Augmentation Translation

End-to-end named entity extraction from speech

no code implementations30 May 2018 Sahar Ghannay, Antoine Caubrière, Yannick Estève, Antoine Laurent, Emmanuel Morin

Until now, NER from speech is made through a pipeline process that consists in processing first an automatic speech recognition (ASR) on the audio and then processing a NER on the ASR outputs.

automatic-speech-recognition Entity Extraction using GAN +3

TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation

2 code implementations12 May 2018 François Hernandez, Vincent Nguyen, Sahar Ghannay, Natalia Tomashenko, Yannick Estève

We present the recent development on Automatic Speech Recognition (ASR) systems in comparison with the two previous releases of the TED-LIUM Corpus from 2012 and 2014.

automatic-speech-recognition End-To-End Speech Recognition +1

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 Language understanding +3

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