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Live TV news and interviews often include multiple individuals speaking, with rapid turn-taking, which makes it difficult for viewers who are Deaf and Hard of Hearing (DHH) to follow who is speaking when reading captions. Prior research has proposed several methods of indicating who is speaking. While recent studies have observed various preferences among DHHviewers for speaker identification methods for videos with different numbers of speakers onscreen, there has not yet been a study that has systematically explored whether there is a formal relationship between the number of people onscreen and the preferences among DHH viewers for how to indicate the speaker in captions.We conducted an empirical study followed by a semi-structured interview with 17 DHH participants to record their preferences among various speaker-identifier types for videos that vary in the number of speakers onscreen. We observed an interaction effect between DHH viewers’ preference for speaker identification and the number of speakers in a video. An analysis of open-ended feedback from participants revealed several factors that influenced their preferences. Our findings guide broadcasters and captioners in selecting speaker-identification methods for captioned videos. 

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. 

Deaf and hard of hearing individuals regularly rely on captioning while watching live TV. Live TV captioning is evaluated by regulatory agencies using various caption evaluation metrics. However, caption evaluation metrics are often not informed by preferences of DHH users or how meaningful the captions are. There is a need to construct caption evaluation metrics that take the relative importance of words in transcript into account. We conducted correlation analysis between two types of word embeddings and human-annotated labelled word-importance scores in existing corpus. We found that normalized contextualized word embeddings generated using BERT correlated better with manually annotated importance scores than word2vec-based word embeddings. We make available a pairing of word embeddings and their human-annotated importance scores. We also provide proof-of-concept utility by training word importance models, achieving an F1-score of 0.57 in the 6-class word importance classification task.