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dc.contributor.author Huenerfauth, Matt en
dc.contributor.author Kacorri, Hernisa en
dc.date.accessioned 2016-11-03T22:14:00Z en
dc.date.available 2016-11-03T22:14:00Z en
dc.date.issued 2016 en
dc.identifier.citation Journal on Technology and Persons with Disabilities 4: 69-78. en
dc.identifier.issn 2330-4219 en
dc.identifier.uri http://hdl.handle.net/10211.3/180115 en
dc.description 31st Annual International Technology and Persons with Disabilities Conference Scientific/Research Proceedings, San Diego, 2016 en
dc.description.abstract Analysis of eyetracking data can serve as an alternative method of evaluation when assessing the quality of computer-synthesized animations of American Sign Language (ASL), technology which can make information accessible to people who are deaf or hard-of-hearing, who may have lower levels of written language literacy. In this work, we build and evaluate the efficacy of descriptive models of subjective scores that native signers assign to ASL animations, based on eye-tracking metrics. en
dc.format application/pdf en
dc.format.extent 11 pages en
dc.language.iso en en
dc.publisher California State University, Northridge. en
dc.rights Copyright 2016 by the authors and California State University, Northridge en
dc.subject Eye-tracking en
dc.subject Sign language en
dc.subject Animation. en
dc.title Eyetracking Metrics Related to Subjective Assessments of ASL Animations en
dc.type Article en
dc.rights.license Creative Commons Attribution-NoDerivs 4.0 Unported License. en


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