Modeling Global Body Configurations in American Sign Language

TL;DR

This paper introduces a probabilistic graphical model to represent and analyze American Sign Language's body configurations, leveraging a new dataset and evaluating its effectiveness in capturing ASL phonetics.

Contribution

We develop a simplified probabilistic graphical model for ASL based on the Movement-Hold model, trained on a novel dataset, enabling better computational understanding of ASL phonetics.

Findings

The PGM effectively models ASL body configurations.

The model provides insights into ASL phonetics.

Evaluation shows competitive performance against other models.

Abstract

American Sign Language (ASL) is the fourth most commonly used language in the United States and is the language most commonly used by Deaf people in the United States and the English-speaking regions of Canada. Unfortunately, until recently, ASL received little research. This is due, in part, to its delayed recognition as a language until William C. Stokoe's publication in 1960. Limited data has been a long-standing obstacle to ASL research and computational modeling. The lack of large-scale datasets has prohibited many modern machine-learning techniques, such as Neural Machine Translation, from being applied to ASL. In addition, the modality required to capture sign language (i.e. video) is complex in natural settings (as one must deal with background noise, motion blur, and the curse of dimensionality). Finally, when compared with spoken languages, such as English, there has been limited research conducted into the linguistics of ASL. We realize a simplified version of Liddell and Johnson's Movement-Hold (MH) Model using a Probabilistic Graphical Model (PGM). We trained our model on ASLing, a dataset collected from three fluent ASL signers. We evaluate our PGM against other models to determine its ability to model ASL. Finally, we interpret various aspects of the PGM and draw conclusions about ASL phonetics. The main contributions of this paper are