Read the Room: Adapting a Robot's Voice to Ambient and Social Contexts

TL;DR

This paper presents a method for adapting robot voice styles to different ambient and social contexts, improving perceived intelligence and appropriateness in human-robot interactions, demonstrated through experiments with the Pepper robot.

Contribution

It introduces a novel process for selecting and testing robot voice styles based on human vocal adaptation in various ambient settings, focusing on social appropriateness and ambiance awareness.

Findings

Voice style impacts perceived robot intelligence factors

Different ambiances require distinct voice styles for social appropriateness

Participants preferred contextually adapted robot voices

Abstract

How should a robot speak in a formal, quiet and dark, or a bright, lively and noisy environment? By designing robots to speak in a more social and ambient-appropriate manner we can improve perceived awareness and intelligence for these agents. We describe a process and results toward selecting robot voice styles for perceived social appropriateness and ambiance awareness. Understanding how humans adapt their voices in different acoustic settings can be challenging due to difficulties in voice capture in the wild. Our approach includes 3 steps: (a) Collecting and validating voice data interactions in virtual Zoom ambiances, (b) Exploration and clustering human vocal utterances to identify primary voice styles, and (c) Testing robot voice styles in recreated ambiances using projections, lighting and sound. We focus on food service scenarios as a proof-of-concept setting. We provide results using the Pepper robot's voice with different styles, towards robots that speak in a contextually appropriate and adaptive manner. Our results with N=120 participants provide evidence that the choice of voice style in different ambiances impacted a robot's perceived intelligence in several factors including: social appropriateness, comfort, awareness, human-likeness and competency.