ASL-Skeleton3D and ASL-Phono: Two Novel Datasets for the American Sign Language
Sign language is an essential resource enabling access to communication and proper socioemotional development for individuals suffering from disabling hearing loss. As this population is expected to reach 700 million by 2050, the importance of the language becomes even more essential as it plays a critical role to ensure the inclusion of such individuals in society. The Sign Language Recognition field aims to bridge the gap between users and non-users of sign languages. However, the scarcity in quantity and quality of datasets is one of the main challenges limiting the exploration of novel approaches that could lead to significant advancements in this research area. Thus, this paper contributes by introducing two new datasets for the American Sign Language: the first is composed of the three-dimensional representation of the signers and, the second, by an unprecedented linguistics-based representation containing a set of phonological attributes of the signs.
Code (0)
등록된 구현이 없습니다.
Tasks
Sign Language RecognitionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
WLASL-LEX: a Dataset for Recognising Phonological Properties in American Sign Language
Signed Language Processing (SLP) concerns the automated processing of signed languages, the main means of communication of Deaf and hearing impaired individuals. SLP features many different tasks, ranging from sign recog…
TranslationWLASL-LEX: a Dataset for Recognising Phonological Properties in American Sign Language
Signed Language Processing (SLP) concerns the automated processing of signed languages, the main means of communication of Deaf and hearing impaired individuals. SLP features many different tasks, ranging from sign recog…
TranslationPhonology Recognition in American Sign Language
Inspired by recent developments in natural language processing, we propose a novel approach to sign language processing based on phonological properties validated by American Sign Language users. By taking advantage of d…
Improving Sign Recognition with Phonology
We use insights from research on American Sign Language (ASL) phonology to train models for isolated sign language recognition (ISLR), a step towards automatic sign language understanding. Our key insight is to explicitl…
Sign Language RecognitionProbing for Phonology in Self-Supervised Speech Representations: A Case Study on Accent Perception
Traditional models of accent perception underestimate the role of gradient variations in phonological features which listeners rely upon for their accent judgments. We investigate how pretrained representations from curr…
Self-Supervised Learning