TY - GEN
T1 - Support in the Moment
T2 - 24th International ACM SIGACCESS Conference on Computers and Accessibility, ASSETS 2022
AU - Hassan, Saad
AU - Amin, Akhter Al
AU - De Lacerda Pataca, Calua
AU - Navarro, Diego
AU - Gordon, Alexis
AU - Lee, Sooyeon
AU - Huenerfauth, Matt
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/10/22
Y1 - 2022/10/22
N2 - As they develop comprehension skills, American Sign Language (ASL) learners often view challenging ASL videos, which may contain unfamiliar signs. Current dictionary tools require students to isolate a single sign they do not understand and input a search query, by selecting linguistic properties or by performing the sign into a webcam. Students may struggle with extracting and re-creating an unfamiliar sign, and they must leave the video-watching task to use an external dictionary tool. We investigate a technology that enables users, in the moment, i.e., while they are viewing a video, to select a span of one or more signs that they do not understand, to view dictionary results. We interviewed 14 American Sign Language (ASL) learners about their challenges in understanding ASL video and workarounds for unfamiliar vocabulary. We then conducted a comparative study and an in-depth analysis with 15 ASL learners to investigate the benefits of using video sub-spans for searching, and their interactions with a Wizard-of-Oz prototype during a video-comprehension task. Our findings revealed benefits of our tool in terms of quality of video translation produced and perceived workload to produce translations. Our in-depth analysis also revealed benefits of an integrated search tool and use of span-selection to constrain video play. These findings inform future designers of such systems, computer vision researchers working on the underlying sign matching technologies, and sign language educators.
AB - As they develop comprehension skills, American Sign Language (ASL) learners often view challenging ASL videos, which may contain unfamiliar signs. Current dictionary tools require students to isolate a single sign they do not understand and input a search query, by selecting linguistic properties or by performing the sign into a webcam. Students may struggle with extracting and re-creating an unfamiliar sign, and they must leave the video-watching task to use an external dictionary tool. We investigate a technology that enables users, in the moment, i.e., while they are viewing a video, to select a span of one or more signs that they do not understand, to view dictionary results. We interviewed 14 American Sign Language (ASL) learners about their challenges in understanding ASL video and workarounds for unfamiliar vocabulary. We then conducted a comparative study and an in-depth analysis with 15 ASL learners to investigate the benefits of using video sub-spans for searching, and their interactions with a Wizard-of-Oz prototype during a video-comprehension task. Our findings revealed benefits of our tool in terms of quality of video translation produced and perceived workload to produce translations. Our in-depth analysis also revealed benefits of an integrated search tool and use of span-selection to constrain video play. These findings inform future designers of such systems, computer vision researchers working on the underlying sign matching technologies, and sign language educators.
KW - ASL Learning
KW - American Sign language
KW - Continuous Signing
KW - Integrated Search
KW - Search Interface
KW - Sign Language Learning
KW - Sign Language Videos
KW - Sign Languages
KW - Sign Look-up
KW - Video Selection
UR - http://www.scopus.com/inward/record.url?scp=85141211335&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85141211335&partnerID=8YFLogxK
U2 - 10.1145/3517428.3544883
DO - 10.1145/3517428.3544883
M3 - Conference contribution
AN - SCOPUS:85141211335
T3 - ASSETS 2022 - Proceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility
BT - ASSETS 2022 - Proceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility
PB - Association for Computing Machinery, Inc
Y2 - 23 October 2022 through 26 October 2022
ER -