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Caption-occlusion severity judgments across live-television genres from deaf and hard-of-hearing viewers.

, , and . W4A, page 26:1-26:12. ACM, (2021)

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Introduction to this Issue., and . TACCESS, 11 (2): 7:1 (2018)Improving Spatial Reference in American Sign Language Animation through Data Collection from Native ASL Signers.. HCI (7), volume 5616 of Lecture Notes in Computer Science, page 530-539. Springer, (2009)Deaf and Hard of Hearing Individuals' Perceptions of Communication with Hearing Colleagues in Small Groups., , , , and . ASSETS, page 271-272. ACM, (2016)Introduction to the Special Issue on Fabrication Technologies and Do-It-Yourself Accessibility., and . TACCESS, 11 (3): 12:1 (2018)American Sign Language Generation: Multimodal NLG with Multiple Linguistic Channels.. ACL, page 37-42. The Association for Computer Linguistics, (2005)Deaf and hard-of-hearing users' prioritization of genres of online video content requiring accurate captions., , and . W4A, page 3:1-3:12. ACM, (2020)Collecting and evaluating the CUNY ASL corpus for research on American Sign Language animation., and . Comput. Speech Lang., 28 (3): 812-831 (2014)Artificial Intelligence Fairness in the Context of Accessibility Research on Intelligent Systems for People who are Deaf or Hard of Hearing., , , , , and . CoRR, (2019)Teaching Inclusive Thinking in Undergraduate Computing., , , and . ASSETS, page 399-400. ACM, (2017)At a Different Pace: Evaluating Whether Users Prefer Timing Parameters in American Sign Language Animations to Differ from Human Signers' Timing., , , and . ASSETS, page 40:1-40:12. ACM, (2021)