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

De Araújo Cardoso, Maria Eduarda
Peres, Sarajane
De Almeida Freitas, Fernando
Venância Barbosa, Felipe
De Moraes Lima, Clodoaldo Aparecido
Hung, Patrick
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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.
Machine Learning, Robotic, and Toy Computing, machine learning, sign language, universal design
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