A User-Powered American Sign Language Dictionary

Danielle Bragg, Kyle Rector, Richard E. Ladner
2015 Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing - CSCW '15  
Students learning American Sign Language (ASL) have trouble searching for the meaning of unfamiliar signs. ASL signs can be differentiated by a small set of simple features including hand shape, orientation, location, and movement. In a feature-based ASL-to-English dictionary, users search for a sign by providing a query, which is a set of observed features. Because there is natural variability in the way signs are executed, and observations are error-prone, an approach other than exact
more » ... of features is needed. We propose ASL-Search, an ASL-to-English dictionary entirely powered by its users. ASL-Search utilizes Latent Semantic Analysis (LSA) on a database of feature-based user queries to account for variability. To demonstrate ASL-Search's viability, we created ASL-Flash, a learning tool that presents online flashcards to ASL students and provides query data. Our simulations on this data serve as a proof of concept, demonstrating that our dictionary's performance improves with use and performs well for users with varied levels of ASL experience.
doi:10.1145/2675133.2675226 dblp:conf/cscw/BraggRL15 fatcat:igs7ncs53rb4fkfbovysl3s6am