Representation Matters: The Case for Diversifying Sign Language Avatars

Abstract

As interest in sign language machine translation grows, there is more focus on developing and refining avatar technology to present translation outputs. While signing avatar technology research has focused on legibility and appearance, there has been little attention paid to representing diversity in signing avatars, and the default is often a white female animation. We present data from focus groups conducted in two ongoing sign language machine translation projects in Europe that give insight into deaf end-users’ desires for diversity in avatar representations. Our results reveal a strong desire for full customisability, including options for representing diversity in gender expression and ethnicity, as well as accommodating sociolinguistic variation and personal identity through modified avatar signing styles. This work provides initial insights, but considerable future research is necessary, particularly with minorities and sub-groups within deaf communities.