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Natural Synthesis of Productive Forms from Structured Descriptions of Sign Language

Authors :
Michael Filhol
John C. McDonald
DePaul University [Chicago]
Information, Langue Ecrite et Signée (ILES)
Laboratoire Interdisciplinaire des Sciences du Numérique (LISN)
Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL)
Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Source :
Machine Translation, Machine Translation, 2021, ⟨10.1007/s10590-021-09272-2⟩
Publication Year :
2021
Publisher :
HAL CCSD, 2021.

Abstract

International audience; Natural animation of Sign Language directly from linguistic descriptions continues to be a challenge especially in cases where the forms involved are more productive, such as geometric depictions. Prior work laid the foundation for natural Sign Language synthesis with the Paula animation system directly from AZee linguistic descriptions. This paper considers more elaborate discourse, composed of several clauses linked together by the overall meaning and involving largely productive signing. We make the case that one of the keys to natural animation of such discourse lies also in the segments between the typically annotated signs, in other words on the segments traditionally termed "transitions". By studying an example discourse video and the corresponding motion capture, we progressively build an efficient linguistic description of it and specify how to animate it naturally.

Details

Language :
English
ISSN :
09226567 and 15730573
Database :
OpenAIRE
Journal :
Machine Translation, Machine Translation, 2021, ⟨10.1007/s10590-021-09272-2⟩
Accession number :
edsair.doi.dedup.....376479e1be174191501ec5b11f3f6e09
Full Text :
https://doi.org/10.1007/s10590-021-09272-2⟩