Computational Linguistics Commons^™

Improving Sign Recognition With Phonology, Lee Kezar, Jesse Thomason, Zed Sevcikova Sehyr

Communication Sciences and Disorders Faculty Articles and Research

We use insights from research on American Sign Language (ASL) phonology to train models for isolated sign language recognition (ISLR), a step towards automatic sign language understanding. Our key insight is to explicitly recognize the role of phonology in sign production to achieve more accurate ISLR than existing work which does not consider sign language phonology. We train ISLR models that take in pose estimations of a signer producing a single sign to predict not only the sign but additionally its phonological characteristics, such as the handshape. These auxiliary predictions lead to a nearly 9% absolute gain in sign recognition …