User experience in speech recognition of navigation devices

Goulati, Areti; Szostak, Dalila

doi:10.1145/2037373.2037451

Cited by 8 publications

(6 citation statements)

References 3 publications

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“…The most commonly discussed potentials include input speed, hands-free interaction, and ability to interact using natural language as input (Kumar et al 2012, Sivaraman et al 2016. Other potentials explored include specific advantages, such as understanding how the elderly and disabled can benefit from VUIs (Pradhan et al 2018, Wulf et al 2014, how hands-free speech interaction can be used while driving a car (Goulati & Szostak 2011, Lee et al 2014, Pfleging et al 2012, and how VUIs can be combined with other modalities such as gesture, multi-touch interaction and eye gaze (Anastasiou et al 2012, Schnelle-Walka & Döweling 2012, Vieira et al 2015.…”

Section: Voice User Interfacesmentioning

confidence: 99%

“…From an HCI perspective, speech interaction and conversational agents have been studied for many years, including the use and usability of VUIs (Myers et al 2018), user expectation and user experience of them (Goulati & Szostak 2011, Luger & Sellen 2016, improved accessibility for people with physical impairments (Corbett & Weber 2016), and design guidelines for speech interaction and VUIs (Murad et al 2018, Murad & Munteanu 2019, Pearl 2016. In recent years, there has been an increase in research studying human interactions with VUIs acting as DPAs, including studies that focus on usability and user experience (Pyae & Joelsson 2018), use by special populations including families and children (Garg & Sengupta 2020, Beneteau et al 2020, Druga et al 2017) people living with disabilities (Pradhan et al 2018) and older adults (Pradhan et al, 2019), personification of digital personal assistants (Lopatovska & Williams 2018, Pradhan et al 2019, Purington et al 2017, racial and socioeconomic impacts on DPA use (Garg & Sengupta, 2019), privacy issues around DPAs (Lau et al 2018), and lack of trust even when the device is not active (Brewster 2018).…”

Section: Introductionmentioning

confidence: 99%

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Digital ethnography of home use of digital personal assistants

Paay

Kjeldskov

Hansen

et al. 2020

Behaviour & Information Technology

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Commercialised voice user interface devices for the home, like Amazon Echo, Google Home, and Apple HomePod, with integrated digital personal assistants have rapidly grown in popularity. These devices embody intelligent software agents that support users in their everyday life through easy and intuitive conversational interactions. While their use in everyday activities is largely unexplored, the proliferation in home use presents a valuable opportunity to add to understanding around the use of in-home digital personal assistants. In this paper, we investigate their home use in a broad context to learn more about people's experiences, attitudes, interactions and expectations with these devices contributing new insights to current knowledge around this use. Applying the digital ethnography method, we collected 3,542 reviews and comments about Amazon Echo, Google Home, and Apple HomePod on Amazon, eBay, and Reddit. Six main themes and 29 categories were derived through filtering, thematic analysis and affinity diagramming. These findings constitute a conceptual framework characterising the current landscape of home use of digital personal assistants. Additionally, we identify and discuss unique issues discovered around the invisible interface, interactive freedom, and creative appropriation. We use our findings to propose implications for interaction design of DPAs for home use.

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Section: Voice User Interfacesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Digital ethnography of home use of digital personal assistants

Paay

Kjeldskov

Hansen

et al. 2020

Behaviour & Information Technology

View full text Add to dashboard Cite

show abstract

“…En conséquence, certaines adaptations informatisées de l'EM se sont attachées à garantir la libre expression de l'utilisateur en lui permettant de s'exprimer en langage naturel [8,14,16]. Cependant, le traitement du langage naturel cause encore de nombreux échecs de compréhension, même lorsqu'il s'agit d'interactions simples [12]. Dans le cadre d'un entretien reposant sur une interaction complexe d'une durée relativement longue, la qualité de l'intervention peut s'en trouver sévèrement affectée [14].…”

Section: Travaux Similairesunclassified

Interaction avec un agent motivationnel pour soutenir le changement de comportement

Louvet

Ravenet

Clavel

2021

32e Conférence Francophone Sur l'Interaction Homme-Machine

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We are developing a virtual agent based on the Motivational Interviewing method to help elderly and inactive individuals change their behavior. The agent engages users in a conversation that aims to highlight reasons why they might want to increase their level of physical activity. In this work, we describe our approach to computerized adaptation of Motivational Interviewing. A study evaluates the outcome of this approach and the system as a whole. CCS CONCEPTS• Human-centered computing → Interaction design; User studies.

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“…The perceived user experience (UX) is another crucial aspect, in particular for speech UIs. This issue is investigated for in-car speech input in [9]. Based on these findings we designed a multimodal approach combining gesture and speech with a focus on UX.…”

Section: Related Workmentioning

confidence: 99%

Multimodal interaction in the car

Pfleging

Schneegaß

Schmidt

2012

Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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Implementing controls in the car becomes a major challenge: The use of simple physical buttons does not scale to the increased number of assistive, comfort, and infotainment functions. Current solutions include hierarchical menus and multi-functional control devices, which increase complexity and visual demand. Another option is speech control, which is not widely accepted, as it does not support visibility of actions, fine-grained feedback, and easy undo of actions. Our approach combines speech and gestures. By using speech for identification of functions, we exploit the visibility of objects in the car (e.g., mirror) and simple access to a wide range of functions equaling a very broad menu. Using gestures for manipulation (e.g., left/right), we provide fine-grained control with immediate feedback and easy undo of actions. In a user-centered process, we determined a set of user-defined gestures as well as common voice commands. For a prototype, we linked this to a car interior and driving simulator. In a study with 16 participants, we explored the impact of this form of multimodal interaction on the driving performance against a baseline using physical buttons. The results indicate that the use of speech and gesture is slower than using buttons but results in a similar driving performance. Users comment in a DALI questionnaire that the visual demand is lower when using speech and gestures.

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User experience in speech recognition of navigation devices

Cited by 8 publications

References 3 publications

Digital ethnography of home use of digital personal assistants

Digital ethnography of home use of digital personal assistants

Interaction avec un agent motivationnel pour soutenir le changement de comportement

Multimodal interaction in the car

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