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.
no code implementations • ACL (IWSLT) 2021 • Hang Le, Florentin Barbier, Ha Nguyen, Natalia Tomashenko, Salima Mdhaffar, Souhir Gabiche Gahbiche, Benjamin Lecouteux, Didier Schwab, Yannick Estève
This paper describes the ON-TRAC Consortium translation systems developed for two challenge tracks featured in the Evaluation Campaign of IWSLT 2021, low-resource speech translation and multilingual speech translation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
no code implementations • IWSLT (ACL) 2022 • Antonios Anastasopoulos, Loïc Barrault, Luisa Bentivogli, Marcely Zanon Boito, Ondřej Bojar, Roldano Cattoni, Anna Currey, Georgiana Dinu, Kevin Duh, Maha Elbayad, Clara Emmanuel, Yannick Estève, Marcello Federico, Christian Federmann, Souhir Gahbiche, Hongyu Gong, Roman Grundkiewicz, Barry Haddow, Benjamin Hsu, Dávid Javorský, Vĕra Kloudová, Surafel Lakew, Xutai Ma, Prashant Mathur, Paul McNamee, Kenton Murray, Maria Nǎdejde, Satoshi Nakamura, Matteo Negri, Jan Niehues, Xing Niu, John Ortega, Juan Pino, Elizabeth Salesky, Jiatong Shi, Matthias Sperber, Sebastian Stüker, Katsuhito Sudoh, Marco Turchi, Yogesh Virkar, Alexander Waibel, Changhan Wang, Shinji Watanabe
The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation.
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.
no code implementations • 14 Mar 2023 • Lucas Maison, Yannick Estève
Thanks to the rise of self-supervised learning, automatic speech recognition (ASR) systems now achieve near-human performance on a wide variety of datasets.
1 code implementation • 20 Feb 2023 • Tuan Nguyen, Salima Mdhaffar, Natalia Tomashenko, Jean-François Bonastre, Yannick Estève
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.
no code implementations • 11 Oct 2022 • Gaëlle Laperrière, Valentin Pelloin, Mickaël Rouvier, Themos Stafylakis, Yannick Estève
In this paper we examine the use of semantically-aligned speech representations for end-to-end spoken language understanding (SLU).
no code implementations • IWSLT (ACL) 2022 • Marcely Zanon Boito, John Ortega, Hugo Riguidel, Antoine Laurent, Loïc Barrault, Fethi Bougares, Firas Chaabani, Ha Nguyen, Florentin Barbier, Souhir Gahbiche, Yannick Estève
This paper describes the ON-TRAC Consortium translation systems developed for two challenge tracks featured in the Evaluation Campaign of IWSLT 2022: low-resource and dialect speech translation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
no code implementations • 4 Apr 2022 • Marcely Zanon Boito, Laurent Besacier, Natalia Tomashenko, Yannick Estève
These models are pre-trained on unlabeled audio data and then used in speech processing downstream tasks such as automatic speech recognition (ASR) or speech translation (ST).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
no code implementations • 2 Apr 2022 • Salima Mdhaffar, Jarod Duret, Titouan Parcollet, Yannick Estève
Our approach is based on the use of an external model trained to generate a sequence of vectorial representations from text.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
1 code implementation • LREC 2022 • Marcely Zanon Boito, Fethi Bougares, Florentin Barbier, Souhir Gahbiche, Loïc Barrault, Mickael Rouvier, Yannick Estève
In this paper we present two datasets for Tamasheq, a developing language mainly spoken in Mali and Niger.
no code implementations • 7 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.
no code implementations • 6 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
Automatic Speech Recognition (ASR)
+2
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
no code implementations • 29 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.
no code implementations • 4 Mar 2021 • Ha Nguyen, Yannick Estève, Laurent Besacier
This paper proposes a decoding strategy for end-to-end simultaneous speech translation.
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.
no code implementations • 18 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.
no code implementations • 30 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.
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.
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.
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.
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
2 code implementations • 12 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
Automatic Speech Recognition (ASR)
+1
no code implementations • 26 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
Automatic Speech Recognition (ASR)
+4