Assessment of Sign Language-Based versus Touch-Based Input for Deaf Users Interacting with Intelligent Personal Assistants
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.
Cite
@article{arxiv.2404.14605,
title = {Assessment of Sign Language-Based versus Touch-Based Input for Deaf Users Interacting with Intelligent Personal Assistants},
author = {Nina Tran and Paige DeVries and Matthew Seita and Raja Kushalnagar and Abraham Glasser and Christian Vogler},
journal= {arXiv preprint arXiv:2404.14605},
year = {2024}
}
Comments
To appear in Proceedings of the Conference on Human Factors in Computing Systems CHI 24, May 11-16, Honolulu, HI, USA, 15 pages. https://doi.org/10.1145/3613904.3642094