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

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dc.contributor.authorDe Araújo Cardoso, Maria Eduarda
dc.contributor.authorPeres, Sarajane
dc.contributor.authorDe Almeida Freitas, Fernando
dc.contributor.authorVenância Barbosa, Felipe
dc.contributor.authorDe Moraes Lima, Clodoaldo Aparecido
dc.contributor.authorHung, Patrick
dc.date.accessioned2020-01-04T07:27:22Z
dc.date.available2020-01-04T07:27:22Z
dc.date.issued2020-01-07
dc.description.abstractNowadays, 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.extent10 pages
dc.identifier.doi10.24251/HICSS.2020.184
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/63923
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMachine Learning, Robotic, and Toy Computing
dc.subjectmachine learning
dc.subjectsign language
dc.subjectuniversal design
dc.titleAutomatic Segmentation of Grammatical Facial Expressions in Sign Language: Towards an Inclusive Communication Experience
dc.typeConference Paper
dc.type.dcmiText

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