Giving Robots a Voice: Human-in-the-Loop Voice Creation and open-ended Labeling
CoRR(2024)
摘要
Speech is a natural interface for humans to interact with robots. Yet,
aligning a robot's voice to its appearance is challenging due to the rich
vocabulary of both modalities. Previous research has explored a few labels to
describe robots and tested them on a limited number of robots and existing
voices. Here, we develop a robot-voice creation tool followed by large-scale
behavioral human experiments (N=2,505). First, participants collectively tune
robotic voices to match 175 robot images using an adaptive human-in-the-loop
pipeline. Then, participants describe their impression of the robot or their
matched voice using another human-in-the-loop paradigm for open-ended labeling.
The elicited taxonomy is then used to rate robot attributes and to predict the
best voice for an unseen robot. We offer a web interface to aid engineers in
customizing robot voices, demonstrating the synergy between cognitive science
and machine learning for engineering tools.
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