Despite the progress in natural language processing of spoken languages, deep learning research for sign languages is still in a very nascent stage. Sign languages occupy a different modality yet have the same characteristics as spoken languages such as grammar, syntax and phonology. With very limited very limited availability of sign language transcriptions, it becomes important to explore unsupervised and weakly supervised techniques that are not reliant on annotated data. Zero-resource language research for segmentation and term discovery in speech has recently started gaining traction and we employ similar methods on the RWTH dataset and analyze their efficacy for sign languages. More importantly by treating sign language utterances no different from speech, we hope to establish ready transferability of research from spoken languages.
Dataset used: RWTH Phoenix 14T
WIP code : Github
