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This content will become publicly available on October 22, 2024

Title: The Sem-Lex Benchmark: Modeling ASL Signs and their Phonemes
Sign language recognition and translation technologies have the potential to increase access and inclusion of deaf signing communities, but research progress is bottlenecked by a lack of representative data. We introduce a new resource for American Sign Language (ASL) modeling, the Sem-Lex Benchmark. The Benchmark is the current largest of its kind, consisting of over 84k videos of isolated sign productions from deaf ASL signers who gave informed consent and received compensation. Human experts aligned these videos with other sign language resources including ASL-LEX, SignBank, and ASL Citizen, enabling useful expansions for sign and phonological feature recognition. We present a suite of experiments which make use of the linguistic information in ASL-LEX, evaluating the practicality and fairness of the Sem-Lex Benchmark for isolated sign recognition (ISR). We use an SL-GCN model to show that the phonological features are recognizable with 85% accuracy, and that they are effective as an auxiliary target to ISR. Learning to recognize phonological features alongside gloss results in a 6% improvement for few-shot ISR accuracy and a 2% improvement for ISR accuracy overall. Instructions for downloading the data can be found at https://github.com/leekezar/SemLex.  more » « less
Award ID(s):
1918556 1625793 1918252
NSF-PAR ID:
10481698
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
ACM
Date Published:
Journal Name:
ASSETS: ACM SIGACCESS Conference On Computers And Accessibility
Issue:
34
ISBN:
9798400702204
Page Range / eLocation ID:
1 - 10
Subject(s) / Keyword(s):
American Sign Language, sign language, phonology, islr, sign recognition
Format(s):
Medium: X
Location:
New York NY USA
Sponsoring Org:
National Science Foundation
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