Patterns for How Users Overcome Obstacles in Voice User Interfaces

@article{Myers2018PatternsFH,
  title={Patterns for How Users Overcome Obstacles in Voice User Interfaces},
  author={Chelsea M. Myers and Anushay Furqan and Jessica Nebolsky and Karina Caro and Jichen Zhu},
  journal={Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems},
  year={2018}
}
Voice User Interfaces (VUIs) are growing in popularity. [...] Key Result We also found patterns that suggest participants were more likely to employ a "guessing" approach rather than rely on visual aids or knowledge recall.Expand
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112 Citations
Highly Influential Citations
3
Background Citations
42
Methods Citations
3
Results Citations
2

112 Citations

Citation Type

Citation Type

Modeling Behavior Patterns with an Unfamiliar Voice User Interface
TLDR
It is found user behavior can be grouped into three clusters: people who become proficient with the system and typically stay proficient while completing different tasks, people who exhibit an exploratory approach to completing tasks, and people who struggled to complete tasks.
The Impact of User Characteristics and Preferences on Performance with an Unfamiliar Voice User Interface
TLDR
The impact of user characteristics and preferences on how users interact with a VUI-based calendar, DiscoverCal, is analyzed by analyzing both VUI usage data and self-reported data to observe correlations between both data types.
Understanding Differences between Heavy Users and Light Users in Difficulties with Voice User Interfaces
TLDR
It is found that heavy users could identify more diverse difficulty types than light users; the types of difficulties that affect each group of users are different, and in particular, the repetition of agent utterance was considered the most inconvenient by heavy users.
“Try, Try, Try Again:” Sequence Analysis of User Interaction Data with a Voice User Interface
TLDR
Using sequence analysis techniques to reveal the patterns of tactics the authors' participants used when interacting with an unfamiliar multi-modal VUI indicates participants initially struggled with understanding the acceptable utterance structure and entities more so than utterance keywords.
Different Types of Voice User Interface Failures May Cause Different Degrees of Frustration
TLDR
An investigation into how different types of failures in a voice user interface (VUI) affects user frustration, which identified three major failure types as perceived by the users, namely, Reason Unknown, Speech Misrecognition, and Utterance Pattern Match Failure.
What Can I Say?: Effects of Discoverability in VUIs on Task Performance and User Experience
TLDR
While no significant differences were found between the strategies, a majority of the participants highlighted their preference for the 'What Can I Say?' strategy if they were to use the VUI more frequently, suggesting designers should consider the use of a discoverability strategy in the design of VUIs.
Adaptive suggestions to increase learnability for voice user interfaces
TLDR
This research focuses on adapting a VUI's spoken feedback to suggest verbal commands to users encountering errors, and guides users to learn what verbal commands execute VUI actions to accomplish desired tasks with the system.
Adaptable Utterances in Voice User Interfaces to Increase Learnability
TLDR
This work proposes adaptable verbal commands, termed adaptable utterances, and Open User Models (OUMs) as a method to allow customization of a VUI's commands to match the individual user’s preference.
“You, Move There!”: Investigating the Impact of Feedback on Voice Control in Virtual Environments
TLDR
It is found that the type of feedback given by agents is critical to user experience, and specifically auditory mechanisms are preferred, allowing users to engage with other modalities seamlessly during interaction.
Reading Between the Guidelines: How Commercial Voice Assistant Guidelines Hinder Accessibility for Blind Users
TLDR
A qualitative document review of VAPA design guidelines published by top commercial vendors Amazon, Google, Microsoft, Apple and Alibaba found that guidelines have many commonalities that surface an underlying assumption that V APA interfaces should be modeled after human-human conversation.
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