Conference proceeding
Patterns for How Users Overcome Obstacles in Voice User Interfaces
PROCEEDINGS OF THE 2018 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2018), v 2018-
01 Jan 2018
Abstract
Voice User Interfaces (VUIs) are growing in popularity. However, even the most current VUIs regularly cause frustration for their users. Very few studies exist on what people do to overcome VUI problems they encounter, or how VUIs can be designed to aid people when these problems occur. In this paper, we analyze empirical data on how users (n=12) interact with our VUI calendar system, DiscoverCal, over three sessions. In particular, we identify the main obstacle categories and types of tactics our participants employ to overcome them. We analyzed the patterns of how different tactics are used in each obstacle category. We found that while NLP Error obstacles occurred the most, other obstacles are more likely to frustrate or confuse the user. We also found patterns that suggest participants were more likely to employ a "guessing" approach rather than rely on visual aids or knowledge recall.
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Details
- Title
- Patterns for How Users Overcome Obstacles in Voice User Interfaces
- Creators
- Chelsea Myers - Drexel UniversityAnushay Furqan - Drexel UniversityJessica Nebolsky - Drexel UniversityKarina Caro - Drexel UniversityJichen Zhu - Drexel UniversityACM
- Publication Details
- PROCEEDINGS OF THE 2018 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2018), v 2018-
- Conference
- 2018 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2018)
- Publisher
- Assoc Computing Machinery
- Number of pages
- 7
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Digital Media
- Web of Science ID
- WOS:000509673100006
- Scopus ID
- 2-s2.0-85046944743
- Other Identifier
- 991019168451704721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Cybernetics
- Computer Science, Information Systems