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Automatic Segmentation of Grammatical Facial Expressions in Sign Language: Towards an Inclusive Communication Experience

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dc.contributor.author De Araújo Cardoso, Maria Eduarda
dc.contributor.author Peres, Sarajane
dc.contributor.author De Almeida Freitas, Fernando
dc.contributor.author Venância Barbosa, Felipe
dc.contributor.author De Moraes Lima, Clodoaldo Aparecido
dc.contributor.author Hung, Patrick
dc.date.accessioned 2020-01-04T07:27:22Z
dc.date.available 2020-01-04T07:27:22Z
dc.date.issued 2020-01-07
dc.identifier.isbn 978-0-9981331-3-3
dc.identifier.uri http://hdl.handle.net/10125/63923
dc.description.abstract Nowadays, natural language processing techniques enable the development of applications that promote communication between humans and between humans and machines. Although the technology related to automated oral communication is mature and affordable, there are currently no appropriate solutions for visual-spatial languages. In the scarce efforts to automatically process sign languages, studies on non-manual gestures are rare, making it difficult to properly interpret the speeches uttered in those languages. In this paper, we present a solution for the automatic segmentation of grammatical facial expressions in sign language. This is a low-cost computational solution designed to integrate a sign language processing framework that supports the development of simple but high value-added applications for the context of universal communication. Moreover, we present a discussion of the difficulties faced by this solution to guide future research in this area.
dc.format.extent 10 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Machine Learning, Robotic, and Toy Computing
dc.subject machine learning
dc.subject sign language
dc.subject universal design
dc.title Automatic Segmentation of Grammatical Facial Expressions in Sign Language: Towards an Inclusive Communication Experience
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2020.184
Appears in Collections: Machine Learning, Robotic, and Toy Computing


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