JMIR Publications

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.