UMD Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/3

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.

More information is available at Theses and Dissertations at University of Maryland Libraries.

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Author: search.filters.author.Pradhan, AlishaSubject: search.filters.subject.conversational interfaces

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    EXPLORING THE ACCESSIBILITY OF HOME-BASED, VOICE-CONTROLLED INTELLIGENT PERSONAL ASSISTANTS
    (2018) Pradhan, Alisha; Lazar, Amanda; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    From an accessibility perspective, home-based, voice-controlled intelligent personal assistants (IPAs) have the potential to greatly expand speech interaction beyond dictation and screenreader output. This research examines the accessibility of off-the-shelf IPAs (e.g., Amazon Echo) by conducting two exploratory studies. To explore the use of IPAs by people with disabilities, we analyzed 346 Amazon Echo reviews mentioning users with disabilities, followed by interviews with 16 visually impaired IPA users. Although some accessibility challenges exist, individuals with a range of disabilities are using IPAs, including unexpected uses such as speech therapy and memory aids. The second study involved a three-week deployment of Echo Dot, a popular IPA, with five older adults who use technology infrequently. Findings indicate preferences for using IPAs over traditional computing devices. We identify design implications to improve IPAs for this population. Both studies highlight issues of discoverability and the need for feature-rich voice-based applications. The findings of this research can inform future work on accessible voice-based IPAs.