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

2026-01-17

AI Summary

This study addresses the gap between laboratory-based robotic voice design and real-world user preferences in public settings. Deploying a guide robot in a shopping mall, the authors conducted a 3×3 factorial experiment over eight days, collecting 725 valid questionnaires and in-depth interviews to systematically investigate how speech rate and volume influence intelligibility, comprehensibility, and likability in an open, dynamic environment. This work presents the first large-scale empirical validation of voice parameter effects on user experience in authentic public spaces. The findings elucidate the contextual mechanisms linking vocal parameters to user perception and propose a context-aware voice adaptation strategy for social robots, offering evidence-based guidelines for designing human-robot spoken interaction in real-world scenarios.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Humans and AI: User Experience and Usability

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• human-robot interaction
• voice design
• context-aware
• real-world deployment
• social robots

Innovation

• context-aware voice design
• naturalistic deployment
• human-robot interaction
• speech rate
• voice parameters