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Communication Dans Un Congrès Année : 2020

Improving and Extending Continuous Sign Language Recognition: Taking Iconicity and Spatial Language into account

Amélioration et extension du champ de la reconnaissance continue de langue des signes: pris en compte de l'iconicité et l'utilisation de l'espace

Résumé

In a lot of recent research, attention has been drawn to recognizing sequences of lexical signs in continuous Sign Language corpora, often artificial. However, as SLs are structured through the use of space and iconicity, focusing on lexicon only prevents the field of Continuous Sign Language Recognition (CSLR) from extending to Sign Language Understanding and Translation. In this article, we propose a new formulation of the CSLR problem and discuss the possibility of recognizing higher-level linguistic structures in SL videos, like classifier constructions. These structures show much more variability than lexical signs, and are fundamentally different than them in the sense that form and meaning can not be disentangled. Building on the recently published French Sign Language corpus Dicta-Sign-LSF-v2, we also discuss the performance and relevance of a simple recurrent neural network trained to recognize illustrative structures.
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Dates et versions

hal-02541816 , version 1 (14-04-2020)

Identifiants

  • HAL Id : hal-02541816 , version 1

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Valentin Belissen, Michèle Gouiffès, Annelies Braffort. Improving and Extending Continuous Sign Language Recognition: Taking Iconicity and Spatial Language into account. LREC2020, 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives, E. Efthimiou, S.-E. Fotinea,T. Hanke, J. A. Hochgesang, J. Kristoffersen, J. Mesch (eds.), 2020, Marseille, France. ⟨hal-02541816⟩
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