I am thrilled to start a two-year NSF-funded postdoctoral research fellowship in with Drs. Georgia Zellou, Zhou Yu, and Katharine Graf Estes to explore human-voice AI interaction.
This project explores the ways in which adults and children adapt their speech when talking to voice-activated digital assistants (e.g., Amazon’s Alexa), compared to adult human interlocutors. This line of work provides a way to test differing theoretical predictions as to the extent that speech-register adjustments are driven by functional motives (e.g., intelligibility) and social factors (e.g., gender). For instance, this research explores whether the same functional motivations that apply when correcting comprehension errors to human interlocutors apply in device-directed speech (DS), such as in manipulating the phonological nature of errors, to carefully control the level of intelligibility-related pressures in communication. At the same time, this project explores how social factors may impact speech adaptation strategies, such as by interlocutor type, speaker age, or device gender. This project additionally involves important methodological innovations in programming and running experiments directly through a digital device platform. Overall, this project aims to fill a gap in our knowledge in the acoustic-phonetic adjustments humans make when talking to voice-AI devices, and can ultimately reveal the underlying mechanisms in speech production by different speakers (e.g., based on age, gender, device experience), contributing to basic science research.