ASL-Skeleton3D and ASL-Phono: Two Novel Datasets for the American Sign Language
This addresses a data bottleneck for researchers in sign language recognition, though it is incremental as it adds new datasets without novel methods.
The paper tackles the scarcity of high-quality datasets in Sign Language Recognition by introducing two new datasets for American Sign Language: ASL-Skeleton3D with 3D representations of signers and ASL-Phono with phonological attributes of signs, but no concrete performance numbers are provided.
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.