Read the Room: Adapting a Robot's Voice to Ambient and Social Contexts
This work addresses the challenge of making robots more socially appropriate and context-aware in interactions, particularly in food service scenarios, though it is incremental as it builds on existing voice adaptation methods.
The study tackled the problem of how a robot should adapt its voice to different ambient and social contexts, such as formal or noisy environments, to improve perceived awareness and intelligence. Results from 120 participants showed that voice style choice impacted perceived intelligence factors like social appropriateness and competency.
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.