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Modeling Word Importance in Conversational Transcripts: Toward improved live captioning for Deaf and hard of hearing viewers.

, , , and . W4A, page 79-83. ACM, (2023)

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Support in the Moment: Benefits and use of video-span selection and search for sign-language video comprehension among ASL learners., , , , , , and . ASSETS, page 29:1-29:14. ACM, (2022)Watch It, Don't Imagine It: Creating a Better Caption-Occlusion Metric by Collecting More Ecologically Valid Judgments from DHH Viewers., , , and . CHI, page 459:1-459:14. ACM, (2022)Q-Nerve: Propagating signal of a damaged nerve using quantum networking., , , , , , and . NSysS, page 1-10. IEEE, (2015)Using BERT Embeddings to Model Word Importance in Conversational Transcripts for Deaf and Hard of Hearing Users., , , and . LT-EDI, page 35-40. Association for Computational Linguistics, (2022)Preferences of Deaf or Hard of Hearing Users for Live-TV Caption Appearance., , , , and . HCI (8), volume 12769 of Lecture Notes in Computer Science, page 189-201. Springer, (2021)Effect of Occlusion on Deaf and Hard of Hearing Users' Perception of Captioned Video Quality., , and . HCI (8), volume 12769 of Lecture Notes in Computer Science, page 202-220. Springer, (2021)Understanding How Deaf and Hard of Hearing Viewers Visually Explore Captioned Live TV News., , , and . W4A, page 54-65. ACM, (2023)Modeling Word Importance in Conversational Transcripts: Toward improved live captioning for Deaf and hard of hearing viewers., , , and . W4A, page 79-83. ACM, (2023)Caption-occlusion severity judgments across live-television genres from deaf and hard-of-hearing viewers., , and . W4A, page 26:1-26:12. ACM, (2021)Who is speaking: Unpacking In-text Speaker Identification Preference of Viewers who are Deaf and Hard of Hearing while Watching Live Captioned Television Program., , , , and . W4A, page 44-53. ACM, (2023)