@inproceedings{10.1145/3491102.3501986,
author = {Hassan, Saad and Amin, Akhter Al and Gordon, Alexis and Lee, Sooyeon and Huenerfauth, Matt},
title = {Design and Evaluation of Hybrid Search for American Sign Language to English Dictionaries: Making the Most of Imperfect Sign Recognition},
year = {2022},
isbn = {9781450391573},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3491102.3501986},
doi = {10.1145/3491102.3501986},
abstract = {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.},
booktitle = {Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems},
articleno = {195},
numpages = {13},
keywords = {Video Search, User Satisfaction, Sign Languages, Search System Design, Search Interfaces, Search Evaluation, IR Effectiveness, Dictionary, American Sign Language (ASL)},
location = {New Orleans, LA, USA},
series = {CHI '22}
}