no code implementations • JEP/TALN/RECITAL 2021 • Diana Nicoleta Popa, William N. Havard, Maximin Coavoux, Eric Gaussier, Laurent Besacier
Le jeu de données SCAN, constitué d’un ensemble de commandes en langage naturel associées à des séquences d’action, a été spécifiquement conçu pour évaluer les capacités des réseaux de neurones à apprendre ce type de généralisation compositionnelle.
no code implementations • CONLL 2020 • William N. Havard, Jean-Pierre Chevrot, Laurent Besacier
The language acquisition literature shows that children do not build their lexicon by segmenting the spoken input into phonemes and then building up words from them, but rather adopt a top-down approach and start by segmenting word-like units and then break them down into smaller units.
no code implementations • CONLL 2019 • William N. Havard, Jean-Pierre Chevrot, Laurent Besacier
In this paper, we study how word-like units are represented and activated in a recurrent neural model of visually grounded speech.
1 code implementation • LREC 2020 • Marcely Zanon Boito, William N. Havard, Mahault Garnerin, Éric Le Ferrand, Laurent Besacier
However, the fact that the source content (the Bible) is the same for all the languages is not exploited to date. Therefore, this article proposes to add multilingual links between speech segments in different languages, and shares a large and clean dataset of 8, 130 parallel spoken utterances across 8 languages (56 language pairs).
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
1 code implementation • 8 Feb 2019 • William N. Havard, Jean-Pierre Chevrot, Laurent Besacier
We investigate the behaviour of attention in neural models of visually grounded speech trained on two languages: English and Japanese.