Unsupervised Term Discovery for Continuous Sign Language

Autor/a: POLAT, Korhan; SARACLAR, Murat
Año: 2020
Editorial: Proceedings of the 9th Workshop on the Representation and Processing of Sign Languages, pages 189–196
Tipo de código: Copyright
Soporte: Digital




Most of the sign language recognition (SLR) systems rely on supervision for training and available annotated sign language resources are scarce due to the difficulties of manual labeling. Unsupervised discovery of lexical units would facilitate the annotation process and thus lead to better SLR systems. Inspired by the unsupervised spoken term discovery in speech processing field, we investigate whether a similar approach can be applied in sign language to discover repeating lexical units. We adapt an algorithm that is designed for spoken term discovery by using hand shape and pose features instead of speech features. The experiments are run on a large scale continuous sign corpus and the performance is evaluated using gloss level annotations. This work introduces a new task for sign language processing that has not been addressed before.