Unsupervised Term Discovery for Continuous Sign Language

Korhan Polat, Murat Saraçlar

Abstract
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.
Anthology ID:
2020.signlang-1.31
Volume:
Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Eleni Efthimiou, Stavroula-Evita Fotinea, Thomas Hanke, Julie A. Hochgesang, Jette Kristoffersen, Johanna Mesch
Venue:
SignLang
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
189–196
Language:
English
URL:
https://aclanthology.org/2020.signlang-1.31
DOI:
Bibkey:
Cite (ACL):
Korhan Polat and Murat Saraçlar. 2020. Unsupervised Term Discovery for Continuous Sign Language. In Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives, pages 189–196, Marseille, France. European Language Resources Association (ELRA).
Cite (Informal):
Unsupervised Term Discovery for Continuous Sign Language (Polat & Saraçlar, SignLang 2020)
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https://preview.aclanthology.org/emnlp22-frontmatter/2020.signlang-1.31.pdf
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