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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 anin situdictionary 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. 

Affective captions employ visual typographic modulations to convey a speaker’s emotions, improving speech accessibility for Deaf and Hard-of-Hearing (dhh) individuals. However, the most effective visual modulations for expressing emotions remain uncertain. Bridging this gap, we ran three studies with 39 dhh participants, exploring the design space of affective captions, which include parameters like text color, boldness, size, and so on. Study 1 assessed preferences for nine of these styles, each conveying either valence or arousal separately. Study 2 combined Study 1’s top-performing styles and measured preferences for captions depicting both valence and arousal simultaneously. Participants outlined readability, minimal distraction, intuitiveness, and emotional clarity as key factors behind their choices. In Study 3, these factors and an emotion-recognition task were used to compare how Study 2’s winning styles performed versus a non-styled baseline. Based on our findings, we present the two best-performing styles as design recommendations for applications employing affective captions. 

Analyzing dance moves and routines is a foundational step in learning dance. Videos are often utilized at this step, and advancements in machine learning, particularly in human-movement recognition, could further assist dance learners. We developed and evaluated a Wizard-of-Oz prototype of a video comprehension tool that offers automatic in-situ dance move identification functionality. Our system design was informed by an interview study involving 12 dancers to understand the challenges they face when trying to comprehend complex dance videos and taking notes. Subsequently, we conducted a within-subject study with 8 Cuban salsa dancers to identify the benefits of our system compared to an existing traditional feature-based search system. We found that the quality of notes taken by participants improved when using our tool, and they reported a lower workload. Based on participants’ interactions with our system, we offer recommendations on how an AI-powered span-search feature can enhance dance video comprehension tools.