Eyetracking Metrics Related to Subjective Assessments of ASL Animations

Abstract

Analysis of eyetracking data can serve as an alternative method of evaluation when assessing the quality of computer-synthesized animations of American Sign Language (ASL), technology which can make information accessible to people who are deaf or hard-of-hearing, who may have lower levels of written language literacy. In this work, we build and evaluate the efficacy of descriptive models of subjective scores that native signers assign to ASL animations, based on eye-tracking metrics.