Practical Insights into Designing Context-Aware Robot Voice Parameters in the Wild

TL;DR

This study investigates how real-world environments influence human perceptions of robot voice, providing insights for designing adaptive, context-aware voice parameters in social robots deployed in public spaces.

Contribution

It offers empirical data from naturalistic studies on how context affects robot voice perception, guiding adaptive voice design in real-world HRI scenarios.

Findings

Context significantly influences voice perception.

Adaptive voice parameters improve user engagement.

Field data informs practical voice design strategies.

Abstract

Voice is an essential modality for human-robot interaction (HRI). The way a robot sounds plays a central role in shaping how humans perceive and engage with it, influencing factors such as intelligibility, understandability, and likability. Although prior work has examined voice design, most studies occur in controlled labs, leaving uncertainty about how results translate to real-world settings. To address this gap, we conducted two naturalistic deployment studies with a guidance robot in a shopping mall: (1) in-depth interviews with six participants, and (2) an eight-day field deployment using a 3x3 design varying speech rate and volume, yielding 725 survey responses. Our results show how real-world context shapes voice perception and inform adaptive, context-aware voice design for social robots in public spaces.