Exploring the Benefits and Applications of Video-Span Selection and Search for Real-Time Support in Sign Language Video Comprehension among ASL Learners

People learning American Sign Language (ASL) and practicing their comprehension skills will often encounter complex ASL videos that may contain unfamiliar signs. Existing dictionary tools require users to isolate a single unknown sign before initiating a search by selecting linguistic properties or performing the sign in front of a webcam. This process presents challenges in extracting and reproducing unfamiliar signs, disrupting the video-watching experience, and requiring learners to rely on external dictionaries. We explore a technology that allows users to select and view dictionary results for one or more unfamiliar signs while watching a video. We interviewed 14 ASL learners to understand their challenges in understanding ASL videos, strategies for dealing with unfamiliar vocabulary, and expectations for an in situ dictionary system. We then conducted an in-depth analysis with eight learners to examine their interactions with a Wizard-of-Oz prototype during a video comprehension task. Finally, we conducted a comparative study with six additional ASL learners to evaluate the speed, accuracy, and workload benefits of an embedded dictionary-search feature within a video player. Our tool outperformed a baseline in the form of an existing online dictionary across all three metrics. The integration of a search tool and span selection offered advantages for video comprehension. Our findings have implications for designers, computer vision researchers, and sign language educators.

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Publication Details

Journal
ACM Transactions on Accessible Computing
Published
2024-09-02
DOI
https://doi.org/10.1145/3690647
Citations
1
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
0.37
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article

Exploring the Benefits and Applications of Video-Span Selection and Search for Real-Time Support in Sign Language Video Comprehension among ASL Learners

Akhter Al Amin, Laleh Nourian, Matt Huenerfauth, Matthew Watkins et al.
1 citations
ACM Transactions on Accessible Computing
Hand Gesture Recognition Systems
0.37
article

Exploring the Benefits and Applications of Video-Span Selection and Search for Real-Time Support in Sign Language Video Comprehension among ASL Learners

Akhter Al Amin, Laleh Nourian, Matt Huenerfauth, Matthew Watkins, Caluã de Lacerda Pataca, Saad Hassan, Alexis Gordon, Garreth W. Tigwell, Sooyeon Lee, Diego Gazquez Navarro
article en
1 citations

Abstract

People learning American Sign Language (ASL) and practicing their comprehension skills will often encounter complex ASL videos that may contain unfamiliar signs. Existing dictionary tools require users to isolate a single unknown sign before initiating a search by selecting linguistic properties or performing the sign in front of a webcam. This process presents challenges in extracting and reproducing unfamiliar signs, disrupting the video-watching experience, and requiring learners to rely on external dictionaries. We explore a technology that allows users to select and view dictionary results for one or more unfamiliar signs while watching a video. We interviewed 14 ASL learners to understand their challenges in understanding ASL videos, strategies for dealing with unfamiliar vocabulary, and expectations for an in situ dictionary system. We then conducted an in-depth analysis with eight learners to examine their interactions with a Wizard-of-Oz prototype during a video comprehension task. Finally, we conducted a comparative study with six additional ASL learners to evaluate the speed, accuracy, and workload benefits of an embedded dictionary-search feature within a video player. Our tool outperformed a baseline in the form of an existing online dictionary across all three metrics. The integration of a search tool and span selection offered advantages for video comprehension. Our findings have implications for designers, computer vision researchers, and sign language educators.

ACM Transactions on Accessible ComputingVol. 17(3)
Tulane University (US), New Jersey Institute of Technology (US), Rochester Institute of Technology (US)
Quality Education
Openalex Percentile: Top 40%
Hand Gesture Recognition Systems
0.37
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