Frequency of Occurrence and Information Entropy of American Sign Language

TL;DR

This study quantifies the information content and redundancy of ASL handshapes, revealing they are less redundant than spoken phonemes, which explains the comparable sentence production times despite slower signing.

Contribution

It provides the first empirical entropy and redundancy analysis of ASL handshapes, linking low redundancy to efficient communication and aiding automated sign recognition.

Findings

ASL handshapes have higher entropy than spoken phonemes.

Low redundancy in handshapes compensates for slower sign production.

Quantitative analysis facilitates advancements in sign language recognition.

Abstract

American Sign Language (ASL) uses a series of hand based gestures as a replacement for words to allow the deaf to communicate. Previous work has shown that although it takes longer to make signs than to say the equivalent words, on average sentences can be completed in about the same time. This leaves unresolved, however, precisely why that should be the case. This paper reports a determination of the empirical entropy and redundancy in the set of handshapes of ASL. In this context, the entropy refers to the average information content in a unit of data. It is found that the handshapes, as fundamental units of ASL, are less redundant than phonemes, the equivalent fundamental units of spoken English, and that their entropy is much closer to the maximum possible information content. This explains why the slower signs can produce sentences in the same time as speaking; the low redundancy compensates for the slow rate of sign production. In addition to this precise quantification, this work is also novel in its approach towards quantifying an aspect of the ASL alphabet. Unlike spoken and written languages, frequency analysis of ASL is difficult due to the fact that every sign is composed of phonemes that are created through a combination of manual and a relatively large and imprecise set of bodily features. Focusing on handshapes as the ubiquitous and universal feature of all sign languages permits a precise quantitative analysis. As interest in visual electronic communication explodes within the deaf community, this work also paves the way for more precise automated sign recognition and synthesis.