Sign Language Recognition Analysis using Multimodal Data

TL;DR

This paper explores a multimodal sign language recognition system combining skeletal and RGB data to improve accessibility for Deaf or Hard-of-Hearing users interacting with voice-controlled devices.

Contribution

It introduces a novel approach using deep learning on skeletal and RGB data for sign language recognition, validated on a large-scale ASL dataset.

Findings

High accuracy on ASL sign recognition

Effective use of skeletal and RGB data combination

Public release of a large ASL dataset for research

Abstract

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.