Improving the efficacy of automated sign language practice tools

Authors:
Helene Brashear
Affiliations:
Georgia Institute of Technology, GVU Center, College of Computing, Atlanta, Georgia
Venue:
ACM SIGACCESS Accessibility and Computing - ASSETS 2007 doctoral consortium
Year:
2007

Citing 5
Cited 0

Toward Scalability in ASL Recognition: Breaking Down Signs into Phonemes

GW '99 Proceedings of the International Gesture Workshop on Gesture-Based Communication in Human-Computer Interaction
A Method for Recognizing a Sequence of Sign Language Words Represented in a Japanese Sign Language Sentence

FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
A gesture-based american sign language game for deaf children

CHI '05 Extended Abstracts on Human Factors in Computing Systems
American sign language recognition in game development for deaf children

Proceedings of the 8th international ACM SIGACCESS conference on Computers and accessibility
Speech and Language Processing (2nd Edition)

Speech and Language Processing (2nd Edition)

Quantified Score

Hi-index	0.00

Visualization

Abstract

CopyCat is an America Sign Language (ASL) game, which uses gesture recognition technology to help young Deaf children practice ASL skills. Our database of signing samples was collected from user studies of Deaf children playing a Wizard of Oz version of the game at the Atlanta Area School for the Deaf. We have created an automatic sign language recognition system for the game. We believe that we can improve the accuracy of this system by characterizing and modeling disfluencies found in the children's signing.