Assessing Virtual Assistant Capabilities with Italian Dysarthric Speech

Ballati, Fabio; Corno, Fulvio; Russis, Luigi De

doi:10.1145/3234695.3236354

Cited by 34 publications

(39 citation statements)

References 8 publications

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“…Results of this study revealed a general speech recognition accuracy of 50% to 60%, with all three systems having similar performance. These findings were partially confirmed with dysarthric Italian patients [ 19 ], where authors found different performance accuracy across the voice assistants.…”

Section: Introductionsupporting

confidence: 54%

“…While insightful, the studies reported above have limitations that might make it challenging to generalize the results. First, the actual speech abilities of the users were not assessed because they were either self-reported [ 7 ] or not reported at all [ 17 , 19 ]. This approach fails to provide clear indications for the design of voice assistants, as it does not highlight the users’ needs.…”

Section: Introductionmentioning

confidence: 99%

“…Some of the previous research [ 17 , 19 ] did not even involve humans as participants, as they relied on prerecorded sentences. While this ensures high reliability in terms of assessing the robustness of the system, it also fails to account for the variability of individual performances and motivation behind the actual use of the device.…”

Section: Introductionmentioning

confidence: 99%

“…Speech and cognitive skills play a significant role in the ability to effectively control voice assistants [ 17 , 19 , 22 ]. To properly convey a voice command, users must adequately control the speed and rhythm of speech.…”

Section: Introductionmentioning

confidence: 99%

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Investigating the Accessibility of Voice Assistants With Impaired Users: Mixed Methods Study

et al. 2020

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Background Voice assistants allow users to control appliances and functions of a smart home 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 (eg, opening a door). The research on voice assistants is mainly dedicated to able-bodied users, and studies evaluating the accessibility of such systems are still sparse and fail to account for the participants’ actual motor, linguistic, and cognitive abilities. Objective The aim of this work is to investigate whether cognitive and/or linguistic functions could predict user performance in operating an off-the-shelf voice assistant (Google Home). Methods A group of users with disabilities (n=16) was invited to a living laboratory and asked to interact with the system. Besides collecting data on their performance and experience with the system, their cognitive and linguistic skills were assessed using standardized inventories. The identification of predictors (cognitive and/or linguistic) capable of accounting 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 For users with disabilities, the effectiveness of interacting with a voice assistant is predicted by the Mini-Mental State Examination (MMSE) and the Robertson Dysarthria Profile (specifically, the ability to repeat sentences), as the best model shows (AIC=130.11). Conclusions Users with motor, linguistic, and cognitive impairments can effectively interact with voice assistants, given specific levels of residual cognitive and linguistic skills. More specifically, our paper advances practical indicators to predict the level of accessibility of speech-based interactive systems. Finally, accessibility design guidelines are introduced based on the performance results observed in users with disabilities.

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Section: Introductionsupporting

confidence: 54%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigating the Accessibility of Voice Assistants With Impaired Users: Mixed Methods Study

et al. 2020

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“…Because dysarthria may deteriorate over time (Kent et al 1999), reaching a level at which speakers cannot produce intelligible speech (Ballati, Corno & De Russis 2018b), and in light of the current knowledge about the performance of VHAs and voice assistants when used by these speakers, I propose a system that allows people with dysarthria to interact with VHAs using nonverbal cues. The system consists of a microphone connected to a Raspberry Pi computer that receives the voice cue from the speaker.…”

Section: Proposed Systemmentioning

confidence: 99%

Virtual Home Assistants for People with Dysarthria

Jaddoh

2020

Electronic Workshops in Computing

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Many individuals with disabilities use virtual home assistants (VHAs) to communicate and execute daily tasks (Pradhan, Mehta & Findlater 2018). Because VHAs are controlled by verbal communication, some groups of users, including people with speech impairments, face barriers to effective interaction with VHAs. My research addresses the problems that individuals with speech impairments, specifically those with dysarthria, experience in interacting with these devices. I propose an alternate technique for interaction using nonverbal voice cues, such as humming, hissing and vowel sounds. The technique requires from the user only the ability to vocalise.

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Smart Speakers for Inclusion: How Can Intelligent Virtual Assistants Really Assist Everybody?

Sciarretta

Alimenti

2021

Lecture Notes in Computer Science

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Assessing Virtual Assistant Capabilities with Italian Dysarthric Speech

Cited by 34 publications

References 8 publications

Investigating the Accessibility of Voice Assistants With Impaired Users: Mixed Methods Study

Investigating the Accessibility of Voice Assistants With Impaired Users: Mixed Methods Study

Virtual Home Assistants for People with Dysarthria

Smart Speakers for Inclusion: How Can Intelligent Virtual Assistants Really Assist Everybody?

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