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Searching for the meaning of an unfamiliar sign-language word in a dictionary is difficult for learners, but emerging sign-recognition technology will soon enable users to search by submitting a video of themselves performing the word they recall. However, sign-recognition technology is imperfect, and users may need to search through a long list of possible results when seeking a desired result. To speed this search, we present a hybrid-search approach, in which users begin with a video-based query and then filter the search results by linguistic properties, e.g., handshape. We interviewed 32 ASL learners about their preferences for the content and appearance of the search-results page and filtering criteria. A between-subjects experiment with 20 ASL learners revealed that our hybrid search system outperformed a video-based search system along multiple satisfaction and performance metrics. Our findings provide guidance for designers of video-based sign-language dictionary search systems, with implications for other search scenarios. 

Despite some prior research and commercial systems, if someone sees an unfamiliar American Sign Language (ASL) word and wishes to look up its meaning in a dictionary, this remains a difficult task. There is no standard label a user can type to search for a sign, and formulating a query based on linguistic properties is challenging for students learning ASL. Advances in sign-language recognition technology will soon enable the design of a search system for ASL word look-up in dictionaries, by allowing users to generate a query by submitting a video of themselves performing the word they believe they encountered somewhere. Users would then view a results list of video clips or animations, to seek the desired word. In this research, we are investigating the usability of such a proposed system, a webcam-based ASL dictionary system, using a Wizard-of-Oz prototype and enhanced the design so that it can support sign language word look-up even when the performance of the underlying sign-recognition technology is low. We have also investigated the requirements of students learning ASL in regard to how results should be displayed and how a system could enable them to filter the results of the initial query, to aid in their search for a desired word. We compared users’ satisfaction when using a system with or without post-query filtering capabilities. We discuss our upcoming study to investigate users’ experience with a working prototype based on actual sign-recognition technology that is being designed. Finally, we discuss extensions of this work to the context of users searching datasets of videos of other human movements, e.g. dance moves, or when searching for words in other languages. 

Advances in sign-language recognition technology have enabled researchers to investigate various methods that can assist users in searching for an unfamiliar sign in ASL using sign-recognition technology. Users can generate a query by submitting a video of themselves performing the sign they believe they encountered somewhere and obtain a list of possible matches. However, there is disagreement among developers of such technology on how to report the performance of their systems, and prior research has not examined the relationship between the performance of search technology and users’ subjective judgements for this task. We conducted three studies using a Wizard-of-Oz prototype of a webcam-based ASL dictionary search system to investigate the relationship between the performance of such a system and user judgements. We found that, in addition to the position of the desired word in a list of results, the placement of the desired word above or below the fold and the similarity of the other words in the results list affected users’ judgements of the system. We also found that metrics that incorporate the precision of the overall list correlated better with users’ judgements than did metrics currently reported in prior ASL dictionary research. 

Much of the world’s population experiences some form of disability during their lifetime. Caution must be exercised while designing natural language processing (NLP) systems to prevent systems from inadvertently perpetuating ableist bias against people with disabilities, i.e., prejudice that favors those with typical abilities. We report on various analyses based on word predictions of a large-scale BERT language model. Statistically significant results demonstrate that people with disabilities can be disadvantaged. Findings also explore overlapping forms of discrimination related to interconnected gender and race identities. 

Environmental sounds can provide important information about surrounding activity, yet recognizing sounds can be challenging for Deaf and Hard-of-Hearing (DHH) individuals. Prior work has examined the preferences of DHH users for various sound-awareness methods. However, these preferences have been observed to vary along some demographic factors. Thus, in this study we investigate the preferences of a specific group of DHH users: current assistive listening devices users. Through a survey of 38 participants, we investigated their challenges and requirements for sound-awareness applications, as well as which type of sounds and what aspects of the sounds are of importance to them. We found that users of assistive listening devices still often miss sounds and rely on other people to obtain information about them. Participants indicated that the importance of awareness of different types of sounds varied according to the environment and the form factor of the sound-awareness technology. Congruent with prior work, participants reported that the location and urgency of the sound were of importance, as well as the confidence of the technology in its identification of that sound. 

Researchers have investigated various methods to help users search for the meaning of an unfamiliar word in American Sign Language (ASL). Some are based on sign-recognition technology, e.g. a user performs a word into a webcam and obtains a list of possible matches in the dictionary. However, developers of such technology report the performance of their systems inconsistently, and prior research has not examined the relationship between the performance of search technology and users' subjective judgements for this task. We conducted two studies using a Wizard-of-Oz prototype of a webcam-based ASL dictionary search system to investigate the relationship between the performance of such a system and user judgements. We found that in addition to the position of the desired word in a list of results, which is what is often reported in literature; the similarity of the other words in the results list also affected users' judgements of the system. We also found that metrics that incorporate the precision of the overall list correlated better with users' judgements than did metrics currently reported in prior ASL dictionary research. 

Developing successful sign language recognition, generation, and translation systems requires expertise in a wide range of fields, including computer vision, computer graphics, natural language processing, human-computer interaction, linguistics, and Deaf culture. Despite the need for deep interdisciplinary knowledge, existing research occurs in separate disciplinary silos, and tackles separate portions of the sign language processing pipeline. This leads to three key questions: 1) What does an interdisciplinary view of the current landscape reveal? 2) What are the biggest challenges facing the field? and 3) What are the calls to action for people working in the field? To help answer these questions, we brought together a diverse group of experts for a two-day workshop. This paper presents the results of that interdisciplinary workshop, providing key background that is often overlooked by computer scientists, a review of the state-of-the-art, a set of pressing challenges, and a call to action for the research community.