  Voice-controlled personal and home assistants (such as the Amazon Echo and
Apple Siri) are becoming increasingly popular for a variety of applications.
However, the benefits of these technologies are not readily accessible to Deaf
or Hard-ofHearing (DHH) users. The objective of this study is to develop and
evaluate a sign recognition system using multiple modalities that can be used
by DHH signers to interact with voice-controlled devices. With the advancement
of depth sensors, skeletal data is used for applications like video analysis
and activity recognition. Despite having similarity with the well-studied human
activity recognition, the use of 3D skeleton data in sign language recognition
is rare. This is because unlike activity recognition, sign language is mostly
dependent on hand shape pattern. In this work, we investigate the feasibility
of using skeletal and RGB video data for sign language recognition using a
combination of different deep learning architectures. We validate our results
on a large-scale American Sign Language (ASL) dataset of 12 users and 13107
samples across 51 signs. It is named as GMUASL51. We collected the dataset over
6 months and it will be publicly released in the hope of spurring further
machine learning research towards providing improved accessibility for digital
assistants.
