ASL-Skeleton3D and ASL-Phono: Two Novel Datasets for the American Sign Language

TL;DR

This paper introduces two new datasets for American Sign Language, one with 3D skeletal data and another with phonological attributes, to advance sign language recognition research.

Contribution

The paper presents two novel datasets for ASL, addressing data scarcity and enabling new research avenues in sign language recognition.

Findings

Datasets facilitate improved sign language recognition models.

First dataset includes 3D skeletal representations of signs.

Second dataset contains phonological attributes of signs.

Abstract

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.