Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Feb 26, 2020
Date Accepted: Jul 26, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Voice assistants are (almost) for everybody: investigating speech-based interactions for users with motor and cognitive impairments
ABSTRACT
Background:
Voice assistants allow users to control appliances and functions of a domotic house by simply uttering a few words. Such systems hold the potential to significantly help users with motor and cognitive disabilities who currently depend on their caregiver even for basic needs, e.g., opening a door. The research on voice assistants is mainly dedicated to abled-bodied users, and studies evaluating the accessibility of such systems are still sparse and fail to account for the participants’ actual cognitive and linguistic abilities.
Objective:
In the present work, we aimed to investigate whether cognitive and/or linguistic functions could predict the user’s performance in operating an off-the-shelf voice assistant, namely Google Home.
Methods:
A group of users with disabilities (N=16) was involved in a living laboratory and was asked to interact with the system. Besides collecting data regarding their performance and their experience with the system, we assessed their cognitive and linguistic skills using standardized inventories. The identification of predictors (cognitive and/or linguistic) capable to account for an efficient interaction with the voice assistant was investigated by performing multiple linear regression models. The best model was identified by adopting a selection strategy based on the Akaike Information Criterion (AIC).
Results:
The best model (AIC = 127.81) shows that, among several cognitive and linguistic predictors, the Mini-Mental State Examination (MMSE) and the ability to repeat sentences (Robertson Dysarthria Profile) were crucial to predict the user’s performance.
Conclusions:
Our findings show that cognitive and linguistic skills are involved and co-participate in the effective interaction with voice assistants. Furthermore, the present study provides design implications based on the mistakes observed during the interaction with voice assistants and advances practical indicators to predict the level of accessibility of voice assistants.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.