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Deep JSLC: A Multimodal Corpus Collection for Data-driven Generation of Japanese Sign Language Expressions

The three-dimensional visualization of spoken or written information in Sign Language (SL) is considered a potential tool for better inclusion of deaf or hard of hearing individuals with low literacy skills. However, conventional technologies for such CG-supported data display are not able to depict all relevant features of a natural signing sequence such as facial expression, spatial references or inter-sign movement, leading to poor acceptance amongst speakers of sign language. The deployment of fully data-driven, deep sequence generation models that proved themselves powerful in speech and text applications might overcome this lack of naturalness. Therefore, we collected a corpus of continuous sentence utterances in Japanese Sign Language (JSL) applicable to the learning of deep neural network models. The presented corpus contains multimodal content information of high resolution motion capture data, video data and both visual and gloss-like mark up annotations obtained with the support of fluent JSL signers. Furthermore, all annotations were encoded under three different encoding schemes with respect to directions, intonation and non-manual information. Currently, the corpus is employed to learn first sequence-to-sequence networks where it shows the ability to train relevant language features.