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Computer Science > Human-Computer Interaction

arXiv:2404.14605 (cs)
[Submitted on 22 Apr 2024]

Title:Assessment of Sign Language-Based versus Touch-Based Input for Deaf Users Interacting with Intelligent Personal Assistants

Authors:Nina Tran, Paige DeVries, Matthew Seita, Raja Kushalnagar, Abraham Glasser, Christian Vogler
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Abstract:With the recent advancements in intelligent personal assistants (IPAs), their popularity is rapidly increasing when it comes to utilizing Automatic Speech Recognition within households. In this study, we used a Wizard-of-Oz methodology to evaluate and compare the usability of American Sign Language (ASL), Tap to Alexa, and smart home apps among 23 deaf participants within a limited-domain smart home environment. Results indicate a slight usability preference for ASL. Linguistic analysis of the participants' signing reveals a diverse range of expressions and vocabulary as they interacted with IPAs in the context of a restricted-domain application. On average, deaf participants exhibited a vocabulary of 47 +/- 17 signs with an additional 10 +/- 7 fingerspelled words, for a total of 246 different signs and 93 different fingerspelled words across all participants. We discuss the implications for the design of limited-vocabulary applications as a stepping-stone toward general-purpose ASL recognition in the future.
Comments: To appear in Proceedings of the Conference on Human Factors in Computing Systems CHI 24, May 11-16, Honolulu, HI, USA, 15 pages. this https URL
Subjects: Human-Computer Interaction (cs.HC)
Cite as: arXiv:2404.14605 [cs.HC]
  (or arXiv:2404.14605v1 [cs.HC] for this version)
  https://doi.org/10.48550/arXiv.2404.14605
Related DOI: https://doi.org/10.1145/3613904.3642094

Submission history

From: Christian Vogler [view email]
[v1] Mon, 22 Apr 2024 22:00:38 UTC (973 KB)
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