Langue des signes

Résumé

thumb|Lettre V en langue des signes. Les langues des signes sont les langues visuo-gestuelles (produites par les mouvements des mains, du visage et du corps dans son ensemble) qui ont émergé au contact des personnes sourdes entre elles et dont l'évolution au cours du temps résulte de la pratique de leurs locuteurs. Elles assurent toutes les fonctions remplies par les langues vocales. Statut naturel des langues des signes Les langues des signes sont des langues naturelles, qui, au même titre que les langues vocales, ont émergé et évolué spontanément au cours du temps par la pratique de leurs locuteurs, sans planification ni prescription consciente ou explicite. Il importe en ce sens de les distinguer du langage parlé complété (LPC) et du français signé, modes de communication créés artificiellement par certains courants pédagogiques. Des recherches linguistiques récentes menées sur deux langues des signes apparues récemment en Palestine confirment l'émergence autonome des

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https://fr.wikipedia.org/wiki/Langue_des_signes

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A Phonology-based Approach for Isolated Sign Production Assessment in Sign Language

Necati Cihan Camgöz, Sandrine Tornay

ACM2020

Chargement

Explainable Phonology-based Approach for Sign Language Recognition and Assessment

Sandrine Tornay

Sign language technology, unlike spoken language technology, is an emerging area of research. Sign language technologies can help in bridging the gap between the Deaf community and the hearing community. One such computer-aided technology is sign language learning technology. To build such a technology, there is a need for sign language technologies that can assess sign production of learners in a linguistically valid manner. Such a technology is yet to emerge. This thesis is a step towards that, where we aim to develop an "explainable" sign language assessment framework. Development of such a framework has some fundamental open research questions: (a) how to effectively model hand movement channel? (b) how to model the multiple channels inherent in sign language? and (c) how to assess sign language at different linguistic levels? The present thesis addresses those open research questions by: (a) development of a hidden Markov model (HMM) based approach that, given only pairwise comparison between signs, derives hand movement subunits that are sharable across sign languages and domains; (b) development of phonology-based approaches, inspired from modeling of articulatory features in speech processing, to model the multichannel information inherent in sign languages in the framework of HMM, and validating it through monolingual, cross-lingual and multilingual sign language recognition studies; and (c) development of a phonology-based sign language assessment approach that can assess in an integrated manner a produced sign at two different levels, namely, lexeme level (i.e., whether the sign production is targeting the correct sign or not) and at form level (i.e. whether the handshape production and the hand movement production is correct or not), and validating it on the linguistically annotated Swiss German Sign Language database SMILE.

EPFL2021

Chargement

HMMs have been the one of the first models to be applied for sign recognition and have become the baseline models due to their success in modeling sequential and multivariate data. Despite the extensive use of HMMs for sign recognition, determining the HMM structure has still remained as a challenge, especially when the number of signs to be modeled is high. In this work, we present a continuous HMM framework for modeling and recognizing isolated signs, which inherently performs model selection to optimize the number of states for each sign separately during recognition. Our experiments on three different datasets, namely, German sign language DGS dataset, Turkish sign language HospiSign dataset and Chalearn14 dataset show that the proposed approach achieves better sign language or gesture recognition systems in comparison to the approach of selecting or presetting the number of HMM states based on k-means, and yields systems that perform competitive to the case where the number of states are determined based on the test set performance.

ACL2020

Chargement