Hey Alexa, why do we use voice assistants? The driving factors of voice assistant technology use

Buteau, Emily; Lee, Joonghwa

doi:10.1080/08824096.2021.1980380

Cited by 35 publications

(15 citation statements)

References 19 publications

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“…Usefulness was theorized to be influenced by PEOU because "the less effortful a system is to use, the more using it can increase job performance" (Venkatesh & Davis, 2000, p. 192). Evidence supports the fact that PEOU has no direct effect on the intention to use but rather an indirect effect through usefulness (Agarwal & Prasad, 1999;Venkatesh, 1999), also in the context of VAs (Buteau & Lee, 2021).…”

Section: Perceived Ease Of Usementioning

confidence: 76%

Shopping with Voice Assistants: How Empathy Affects Individual and Family Decision-Making Outcomes

2023

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Section: Perceived Ease Of Usementioning

confidence: 76%

Shopping with Voice Assistants: How Empathy Affects Individual and Family Decision-Making Outcomes

2023

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“…Despite other companies such as Amazon, Samsun, and others providing similar services, Apple Inc. stands as one of the most preferred ones. Accompanied by the ease of use, from basic to complex level services, Siri provides support to its users, indicating the growing importance of voice assistants in the mobile computing arena [87]- [91].…”

Section: Ipma Discussion On Resultsmentioning

confidence: 99%

Factors Behind Virtual Assistance Usage Among iPhone Users: Theory of Reasoned Action

Farhi

Jeljeli²,

Zahra

et al. 2023

Int. J. Interact. Mob. Technol.

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Smartphone usage is increasing due to several features and opportunities offered by manufacturers and service providers. Existing literature attributes this increased usage to the availability of virtual assistants, mainly voice assistants, that ensure users' facility and maximum benefit. This research also focused on the factors behind voice assistant usage, particularly in iPhones among users in the United Arab Emirates. Notably, the conceptual framework in this research is supported by the Theory of Reasoned Action. The researchers adopted a cross-sectional design and gathered data from n= 251 respondents. Data analysis through Structural Equation Modelling revealed that, Perceived Ease of Use, Subjective Norms, and Perceived Behavioral Control work as significant factors contributing to iPhone-based voice assistant usage. On the other hand, Expectation Confirmation also works as a significant factor in Siri usage. On the other hand, Satisfaction significantly mediated (p> .022) the users’ Expectation Confirmation leading to Siri usage among the respondents. Further, the Artificial Neural Network Analysis (ANN) revealed an overall accuracy regarding Training at 14.8% and 30.8% concerning Testing. Thus, it is concluded that the devices accompanied by ease of use are preferred due to their distinguished services. In this regard, highlighting voice assistants' help also reflects the importance of technology in our daily lives. Especially mobile computing is playing a significant role in improving our everyday technology experiences.

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“…Voice assistants have been applied for performing various tasks, such as schedule management, home appliance control, and financial transactions, via voice commands [7]. With the rapid increase in voice assistant application market size, interest in speaker recognition technology that allows voice assistants to respond only to registered voices has increased [8].…”

Section: A Voice Spoofing Attackmentioning

confidence: 99%

Experimental Case Study of Self-Supervised Learning for Voice Spoofing Detection

et al. 2023

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This study aims to improve the performance of voice spoofing attack detection through self-supervised pretraining. Supervised learning needs appropriate input variables and corresponding labels for constructing the machine learning models that are to be applied. It is necessary to secure a large number of labeled datasets to improve the performance of supervised learning processes. However, labeling requires substantial inputs of time and effort. One of the methods for managing this requirement is self-supervised learning, which uses pseudo-labeling without the necessity for substantial human input. This study experimented with contrastive learning, a well-performing self-supervised learning approach, to construct a voice spoofing detection model. We applied MoCo's dynamic dictionary, SimCLR's symmetric loss, and COLA's bilinear similarity in our contrastive learning framework. Our model was trained using VoxCeleb data and voice data extracted from YouTube videos. Our self-supervised model improved the performance of the baseline model from 6.93% to 5.26% for a logical access (LA) scenario and improved the performance of the baseline model from 0.60% to 0.40% for a physical access (PA) scenario.

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Hey Alexa, why do we use voice assistants? The driving factors of voice assistant technology use

Cited by 35 publications

References 19 publications

Shopping with Voice Assistants: How Empathy Affects Individual and Family Decision-Making Outcomes

Shopping with Voice Assistants: How Empathy Affects Individual and Family Decision-Making Outcomes

Factors Behind Virtual Assistance Usage Among iPhone Users: Theory of Reasoned Action

Experimental Case Study of Self-Supervised Learning for Voice Spoofing Detection

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