A Method for Generating a Non-Manual Feature Model for Sign Language Processing

While recent approaches to sign language processing have shifted to the domain of Machine Learning (ML), the treatment of Non-Manual Features (NMFs) remains an open question. The principal challenge facing this method is the comparatively small sign language corpora available for training machine learning models.This study produces a statistical model which may be used in future ML, rules-based, and hybrid-learning approaches for sign language processing tasks. In doing so, this research explores the emerging patterns of non-manual articulation concerning grammatical classes in Irish Sign Language (ISL). The experimental method applied here is a novel implementation of an association rules mining approach to a sign language dataset consisting of NMF and grammatical class data from the Signs of Ireland corpus.Our analysis of association rules has identified patterns between grammatical classes and various non-manual articulations. One such pattern discovery is the strong correlation between various NMFs and depicting verbs. Indeed, this study reports that the less lexicalised a sign is, the more likely it is to use NMFs.Findings from this work will inform future research on NMF treatment in sign language processing, while the statistical model may be utilised by such systems in the future.