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

TL;DR

This paper reviews the interdisciplinary landscape of sign language recognition, generation, and translation, highlighting challenges and calling for integrated research efforts across fields to advance the technology.

Contribution

It presents insights from a multidisciplinary workshop, synthesizing current state-of-the-art, challenges, and actionable recommendations for future research in sign language processing.

Findings

Identifies key interdisciplinary challenges in sign language tech

Highlights gaps in current research approaches

Provides a set of calls to action for the community

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.