voice user interface
155902394
Hey Alexa examine the variables influencing the use of artificial intelligent in home voice assistants
Abstract Artificial Intelligent (AI) In home Voice Assistants have seen unprecedented growth. However, we have little understanding on the factors motivating individuals to use such devices. Given the unique characteristics of the technology, in the main hands free, controlled by voice, and the presentation of a voice user interface, the current technology adoption models are not comprehensive enough to explain the adoption of this new technology. Focusing on voice interactions, this research combines the theoretical foundations of U> with technology theories to gain a clearer understanding on the motivations for adopting and using in home voice assistants. This research presents a conceptual model on the use of voice controlled technology and an empirical validation of the model through the use of Structural Equation Modelling with a sample of 724 in home voice assistant users. The findings illustrate that individuals are motivated by the (1) utilitarian benefits, (2) symbolic benefits and (3) social benefits provided by voice assistants, the results found that hedonic benefits only motivate the use of in home voice assistants in smaller households. Additionally, the research establishes a moderating role of perceived privacy risks in dampening and negatively influencing the use of in home voice assistants.
Comput. Hum. Behav.
Graeme McLean Kofi Osei-Frimpong
202698936
To Please in a Pod: Employing an Anthropomorphic Agent Interlocutor to Enhance Trust and User Experience in an Autonomous, Self Driving Vehicle
Recognising that one of the aims of conversation is to build, maintain and strengthen positive relationships with others, the study explores whether passengers in an autonomous vehicle display similar behaviour during transactions with an on board conversational agent interface; moreover, whether related attributes (e.g. trust) transcend to the vehicle itself. Employing a counterbalanced, within subjects design, thirty four participants were transported in a self driving pod using an expansive testing arena. Participants undertook three journeys with an anthropomorphic agent interlocutor (via Wizard of Oz) a voice command interface, or a traditional touch surface; each delivered equivalent task related information. Results show that the agent interlocutor was the most preferred interface, attracting the highest ratings of trust, and significantly enhancing the pleasure and sense of control over the journey experience, despite the inclusion of 'trust challenges' as part of the design. The findings can help support the design and development of in vehicle agent based voice interfaces to enhance trust and user experience in autonomous cars.
AutomotiveUI
David R Large Kyle Harrington Gary E Burnett Jacob Luton Peter Thomas Pete Bennett
69742277
An Integrated Model of Voice User Interface Continuance Intention: The Gender Effect
ABSTRACT Advanced speech recognition technology facilitated the development of voice based smart devices. Voice user interface (VUI) is now a common feature on smartphones, computers, smart home devices, and car systems. The fragmented and context focused literature on VUI motivates this examination of the relationship between perceived quality and customer satisfaction in VUI portable devices. This study is the first to introduce extrinsic motivational factors as an extension to Wixom and Todd's model. These additional antecedent factors add an enriched explanation of VUI user behavior. This study is also the first to consider the role of gender in a VUI behavior model. Our findings suggest that the proposed driving factors, including trust, perceived risks, perceived enjoyment, and mobile self efficacy, significantly affect VUI user attitudes, which influence their continuance intention. Our results also address the role of gender on the association of attitude toward VUI use and its antecedents. The findings show that perceived risk (privacy concerns) and perceived ease of use are more influential on VUI use behavior of males than females. However, the effect of trust and mobile self efficacy play a more crucial role for females than males.
Int. J. Hum. Comput. Interact.
Quynh-Anh N Nguyen Anh Ta Victor R Prybutok
199537315
"I don't know what you're talking about, HALexa" the case for voice user interface guidelines
As Voice User Interfaces (VUI) grow in popularity in both the research and academic world, designers are met with new challenges in delivering on the promises of voice interaction. These promises depict a world where one can just speak to their devices, akin to HAL 9000; yet, existing usability challenges still leave many disappointed. These challenges often make or break the experience users have with VUIs. We argue that what we are missing is a foundation on which to build (and deliver) our promises: it is essential to build a foundation of VUI principles that can guide future designers in the development of voice interaction. We must address the lack of research in developing foundational VUI specific guidelines that can aid designers in meeting the expectations and promises of true voice interaction.
CUI
Christine Murad Cosmin Munteanu
196613869
Development of a Heuristic Evaluation Tool for Voice User Interfaces
Voice user interfaces (VUIs) are now a common means of interaction with IT systems. To assist in evaluating the usability of such interfaces, a set of evaluation heuristics assessing speech interfaces was developed by following an existing methodology for defining usability heuristics. Two groups of eight participants conducted an evaluation by inspection of three speech based systems, a mobile phone assistant, a smart speaker and an in car hands free phone system. One group used Nielsen and Molich's general heuristics for user interface design while the other group used the VUI heuristics. The second group found, on average, more problems than the first group. However, most heuristics from both sets were rated as useful during the study. This indicates that a mixture of both general and application specific heuristics are needed for a comprehensive evaluation to be performed. Experience from a pilot study, where a smart speaker was set up in a domestic setting, highlighted the need to also consider social and environmental issues to gain a complete picture of user experience when interacting with speech systems.
HCI
Martin C Maguire
189926717
WaveEar: Exploring a mmWave based Noise resistant Speech Sensing for Voice User Interface
Voice user interface (VUI) has become an integral component in modern personal devices \textite.g. smartphones, voice assistant) by fundamentally evolving the information sharing between the user and device. Acoustic sensing for VUI is designed to sense all acoustic objects; however, the existing VUI mechanism can only offer low quality speech sensing. This is due to the audible and inaudible interference from complex ambient noise that limits the performance of VUI by causing denial of service (DoS) of user requests. Therefore, it is of paramount importance to enable noise resistant speech sensing in VUI for executing critical tasks with superior efficiency and precision in robust environments. To this end, we investigate the feasibility of employing radio frequency signals, such as millimeter wave (mmWave) for sensing the noise resistant voice of an individual. We first perform an in depth study behind the rationale of voice generation and resulting vocal vibrations. From the obtained insights, we presentWaveEar, an end to end noise resistant speech sensing system.WaveEar comprises a low cost mmWave probe to localize the position of the speaker among multiple people and direct the mmWave signals towards the near throat region of the speaker for sensing his/her vocal vibrations. The received signal, containing the speech information, is fed to our novel deep neural network for recovering the voice through exhaustive extraction. Our experimental evaluation under real world scenarios with 21 participants shows the effectiveness ofWaveEar to precisely infer the noise resistant voice and enable a pervasive VUI in modern electronic devices.
MobiSys
Chenhan Xu Zhengxiong Li Hanbin Zhang Aditya Singh Rathore Huining Li Chen Song Kun Wang Wenyao Xu
144207738
TurtleTalk: An Educational Programming Game for Children with Voice User Interface
Interest in programming education for children is growing. This research explores the possibilities of utilizing voice user interface (VUI) in children's programming education. We designed an interactive educational programming game called TurtleTalk, which converts the various utterances of children into code using a neural network and displays the results on a screen (Figure 1) Through VUI, children can move the turtle, the voice agent of the game, to the target location and learn the basic programming concepts of "sequencing" and "iteration." We conducted a preliminary user study where eight children played the game and took part in a posthoc interview. The results showed that voice interaction with TurtleTalk led children to be more immersed in the game and understand the elements of programming with ease and confidence.
CHI Extended Abstracts
Hyunhoon Jung Hee Jae Kim Seongeun So Jinjoong Kim Changhoon Oh
140214172
The Impact of User Characteristics and Preferences on Performance with an Unfamiliar Voice User Interface
Voice User Interfaces (VUIs) are increasing in popularity. However, their invisible nature with no or limited visuals makes it difficult for users to interact with unfamiliar VUIs. We analyze the impact of user characteristics and preferences on how users interact with a VUI based calendar, DiscoverCal. While recent VUI studies analyze user behavior through self reported data, we extend this research by analyzing both VUI usage data and self reported data to observe correlations between both data types. Results from our user study (n=50) led to four key findings: 1) programming experience did not have a wide spread impact on performance metrics while 2) assimilation bias did, 3) participants with more technical confidence exhibited a trial and error approach, and 4) desiring more guidance from our VUI correlated with performance metrics that indicate cautious users.
CHI
Chelsea M Myers Anushay Furqan Jichen Zhu
108291420
A Near Real Time Automatic Speaker Recognition Architecture for Voice Based User Interface
In this paper, we present a novel pipelined near real time speaker recognition architecture that enhances the performance of speaker recognition by exploiting the advantages of hybrid feature extraction techniques that contain the features of Gabor Filter (GF) Convolution Neural Networks (CNN) and statistical parameters as a single matrix set. This architecture has been developed to enable secure access to a voice based user interface (UI) by enabling speaker based authentication and integration with an existing Natural Language Processing (NLP) system. Gaining secure access to existing NLP systems also served as motivation. Initially, we identify challenges related to real time speaker recognition and highlight the recent research in the field. Further, we analyze the functional requirements of a speaker recognition system and introduce the mechanisms that can address these requirements through our novel architecture. Subsequently, the paper discusses the effect of different techniques such as CNN, GF, and statistical parameters in feature extraction. For the classification, standard classifiers such as Support Vector Machine (SVM) Random Forest (RF) and Deep Neural Network (DNN) are investigated. To verify the validity and effectiveness of the proposed architecture, we compared different parameters including accuracy, sensitivity, and specificity with the standard AlexNet architecture.
Mach. Learn. Knowl. Extr.
Parashar Dhakal Praveen Damacharla Ahmad Y Javaid Vijay Kumar Devabhaktuni
202602838
Implementing the ATLAS Self Driving Vehicle Voice User Interface
This article describes the implementation of the ATLAS self driving vehicle voice user interface prototype. ATLAS has been designed to support the experiential needs of visually impaired self driving vehicle users.
Julian Brinkley