Filipino Sign Language (FSL) is a multi-modal language that is composed of manual signlas and non-manual signals. Very minimal research is done regarding non-manual signals (Martinez and Cabalfin, 2008) despite the fact that non-manual signals play a significant role in conversations as it can be mixed freely with manual signals (Cabalfin et al., 2012). For other Sign Languages, there have been numerous researches regarding non-manual; however, most of these focused on the semantic and/or lexical functions only. Research on facial expressions in sign language that convey emotions or feelings and degrees of adjectives is very minimal. In this research, an analysis and recognition of non-manual signals in Filipino Sign Language are performed. The non-manual signals included are Types of Sentences (i.e. Statement, Question, Exclamation), Degrees of Adjectives (i.e. Absence, Presence, High Presence), and Emotions (i.e. Happy, Sad, Fast-approaching danger, stationary danger). The corpus was built with the help of the FSL Deaf Professors, and the 5 Deaf participants who signed 5 sentences for each of the types in front of Microsoft Kinect sensor. Genetic Algorithm is applied for the feature selection, while Artificial Neural Network and Support Vector Machine is applied for classification.
@InProceedings{RIVERA18.18040, author = {Joanna Pauline Rivera and Clement Ong}, title = {Recognizing Non-manual Signals in Filipino Sign Language}, booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {may}, date = {7-12}, location = {Miyazaki, Japan}, editor = {Mayumi Bono and Eleni Efthimiou and
Stavroula-Evita Fotinea and Thomas Hanke and
Julie Hochgesang and Jette Kristoffersen and
Johanna Mesch and Yutaka Osugi}, publisher = {European Language Resources Association (ELRA)}, address = {Paris, France}, isbn = {979-10-95546-01-6}, language = {english} }
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