Exploring automatic text-to-sign translation in a healthcare setting

Author:

Esselink LykeORCID,Roelofsen Floris,Dotlačil Jakub,Mende-Gillings Shani,de Meulder Maartje,Sijm Nienke,Smeijers Anika

Abstract

AbstractCommunication between healthcare professionals and deaf patients has been particularly challenging during the COVID-19 pandemic. We have explored the possibility to automatically translate phrases that are frequently used in the diagnosis and treatment of hospital patients, in particular phrases related to COVID-19, from Dutch or English to Dutch Sign Language (NGT). The prototype system we developed displays translations either by means of pre-recorded videos featuring a deaf human signer (for a limited number of sentences) or by means of animations featuring a computer-generated signing avatar (for a larger, though still restricted number of sentences). We evaluated the comprehensibility of the signing avatar, as compared to the human signer. We found that, while individual signs are recognized correctly when signed by the avatar almost as frequently as when signed by a human, sentence comprehension rates and clarity scores for the avatar are substantially lower than for the human signer. We identify a number of concrete limitations of the JASigning avatar engine that underlies our system. Namely, the engine currently does not offer sufficient control over mouth shapes, the relative speed and intensity of signs in a sentence (prosody), and transitions between signs. These limitations need to be overcome in future work for the engine to become usable in practice.

Funder

ZonMw

H2020 European Research Council

Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Human-Computer Interaction,Information Systems,Software

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