Impact Analysis of the Use of Speech and Language Models Pretrained by Self-Supersivion for Spoken Language Understanding

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.

Language Modelling Self-Supervised Learning +3

The Spoken Language Understanding MEDIA Benchmark Dataset in the Era of Deep Learning: data updates, training and evaluation tools

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.

Intent Detection Spoken Language Understanding

Africa-Centric Self-Supervised Pre-Training for Multilingual Speech Representation in a Sub-Saharan Context

no code implementations • 2 Apr 2024 • Antoine Caubrière, Elodie Gauthier

We present the first self-supervised multilingual speech model trained exclusively on African speech.

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

no code implementations • 24 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 Automatic Speech Recognition (ASR) +7

End2End Acoustic to Semantic Transduction

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.

Language Modelling Spoken Language Understanding

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 Decoder +1

Curriculum-based transfer learning for an effective end-to-end spoken language understanding and domain portability

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.

POS POS Tagging +2

End-to-end named entity extraction from speech

no code implementations • 30 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 Automatic Speech Recognition (ASR) +5