Giving robots a voice: Human-in-the-loop voice creation and open-ended labeling

van Rijn, Pol; Mertes, Silvan; Janowski, Kathrin; Weitz, Katharina; Jacoby, Nori; André, Elisabeth

doi:10.1145/3613904.3642038

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Giving robots a voice: Human-in-the-loop voice creation and open-ended labeling

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van Rijn, Pol
Department of Neuroscience, Max Planck Institute for Empirical Aesthetics, Max Planck Society;

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Jacoby, Nori
Research Group Computational Auditory Perception, Max Planck Institute for Empirical Aesthetics, Max Planck Society;

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引用

van Rijn, P., Mertes, S., Janowski, K., Weitz, K., Jacoby, N., & André, E. (2024). Giving robots a voice: Human-in-the-loop voice creation and open-ended labeling. In F. F., Mueller, P., Kyburz, J. R., Williamson, C., Sas, M. L., Wilson, P. T., Dugas, & I., Shklovski (Eds.), CHI '24: Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1-34). doi:10.1145/3613904.3642038.

引用: https://hdl.handle.net/21.11116/0000-000F-600C-8

要旨

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.