Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective

Abstract

Developing successful sign language recognition, generation, and translation systems requires expertise in a wide range of fields, including computer vision, computer graphics, natural language processing, human-computer interaction, linguistics, and Deaf culture. Despite the need for deep interdisciplinary knowledge, existing research occurs in separate disciplinary silos, and tackles separate portions of the sign language processing pipeline. This leads to three key questions: 1) What does an interdisciplinary view of the current landscape reveal? 2) What are the biggest challenges facing the field? and 3) What are the calls to action for people working in the field? To help answer these questions, we brought together a diverse group of experts for a two-day workshop. This paper presents the results of that interdisciplinary workshop, providing key background that is often overlooked by computer scientists, a review of the state-of-the-art, a set of pressing challenges, and a call to action for the research community.

Cited by in corpus (8)

• AUTSL: A Large Scale Multi-modal Turkish Sign Language Dataset and Baseline Methods
• A Comprehensive Study on Deep Learning-based Methods for Sign Language Recognition
• Social App Accessibility for Deaf Signers
• Effect of Kinematics and Fluency in Adversarial Synthetic Data Generation for ASL Recognition with RF Sensors
• Sign-to-Speech Model for Sign Language Understanding: A Case Study of Nigerian Sign Language
• KinePose: A temporally optimized inverse kinematics technique for 6DOF human pose estimation with biomechanical constraints
• Data Augmentation for Sign Language Gloss Translation
• Read and Attend: Temporal Localisation in Sign Language Videos