HCII PhD Defense: Julia Cambre
When
-
Description
Designing for Voice in Context
Julia Cambre
HCII Ph.D. Thesis Defense
Time & Location
Monday, September 9, 2024 at 10am ET
Newell-Simon Hall (NSH) 3305
Meeting ID: 91333297550
Passcode: 657255
Thesis Committee
Chinmay Kulkarni (Chair) - Emory University & Google
Jeff Bigham - Carnegie Mellon University
Nik Martelaro - Carnegie Mellon University
Amy Ogan - Carnegie Mellon University
Andrés Monroy-Hernández, Princeton University
Abstract
Voice interfaces are all around us. While they hold the potential to offer hands-free convenience and provide a natural interface for interacting with our devices, the voice interfaces that are most common today---voice assistants such as Alexa, Siri, and the Google Assistant---fall short of the promise that voice provides: users make use of only a small fraction of the functionality that voice assistants offer, and many struggle with more fundamental challenges such as speech recognition failures and unmet expectations of the assistants' capabilities due to their human-like personalities and voices. I claim that these shortcomings of voice interfaces are also a consequence of the largely "one size fits all" approach that we see in many voice-enabled technologies today, where the same voice assistant is deployed across a wide range of contexts with minimal tailoring. In practice, this tendency to embed an assistant in a variety of devices and potential interaction scenarios can result in them lacking a solid understanding of the complex physical and social environments in which they are used.
As an alternative to this "one size fits all" approach, my thesis work advocates for designing voice interfaces that are more deeply situated within the context of interaction. I approach this through a range of explorations in the voice space, combining both systems-based research and user evaluations in which I prototype and test voice interfaces in novel contexts that have included a biology wet lab and a large scale deployment within the Firefox web browser, as well as design-based research in which I explore possible futures for voice interfaces across domains like the home, workplace, and transit. These projects culminate with the development and study of LUCA (Latent Understanding of Context Assistant), a voice assistant prototype that probes the future of how such assistants might adapt as the context of interaction changes. LUCA demonstrates how even a small amount of context data, such as the location, weather, and time, can yield substantial improvements over the status quo offered by today's most common voice assistants.
The overarching goals of and findings from this thesis point to a need for a tighter coupling between users' mental models of what a voice interface is capable of (e.g. based on the task, physical surroundings, and data sources) and its actual functionality, with contextually designed assistants helping to scope users’ expectations of the interface's capabilities more appropriately, and therefore yielding more successful interactions as a result. Through reflections across all the projects that comprise this thesis, I introduce a framework for contextual voice design and contribute a series of insights as to what voice interfaces should know about their context to provide more accurate, relevant results and a better user experience.
As an alternative to this "one size fits all" approach, my thesis work advocates for designing voice interfaces that are more deeply situated within the context of interaction. I approach this through a range of explorations in the voice space, combining both systems-based research and user evaluations in which I prototype and test voice interfaces in novel contexts that have included a biology wet lab and a large scale deployment within the Firefox web browser, as well as design-based research in which I explore possible futures for voice interfaces across domains like the home, workplace, and transit. These projects culminate with the development and study of LUCA (Latent Understanding of Context Assistant), a voice assistant prototype that probes the future of how such assistants might adapt as the context of interaction changes. LUCA demonstrates how even a small amount of context data, such as the location, weather, and time, can yield substantial improvements over the status quo offered by today's most common voice assistants.
The overarching goals of and findings from this thesis point to a need for a tighter coupling between users' mental models of what a voice interface is capable of (e.g. based on the task, physical surroundings, and data sources) and its actual functionality, with contextually designed assistants helping to scope users’ expectations of the interface's capabilities more appropriately, and therefore yielding more successful interactions as a result. Through reflections across all the projects that comprise this thesis, I introduce a framework for contextual voice design and contribute a series of insights as to what voice interfaces should know about their context to provide more accurate, relevant results and a better user experience.
Document
Hope to see you there!