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

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, et al. (Eds.), CHI '24: Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1-34). doi:10.1145/3613904.3642038.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000F-600C-8 Version Permalink: https://hdl.handle.net/21.11116/0000-000F-600D-7
Genre: Conference Paper

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24-cap-jac-03-giving.pdf (Publisher version), 15MB
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Copyright Date:
2024
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This work is licensed under a Creative Commons Attribution International 4.0 License. CHI ’24, May 11–16, 2024, Honolulu, HI, USA © 2024 Copyright held by the owner/author(s).
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https://creativecommons.org/licenses/by/4.0/

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 Creators:
van Rijn, Pol1, Author                 
Mertes, Silvan2, Author
Janowski, Kathrin2, Author
Weitz, Katharina2, Author
Jacoby, Nori3, Author                 
André, Elisabeth2, Author
Affiliations:
1Department of Neuroscience, Max Planck Institute for Empirical Aesthetics, Max Planck Society, ou_2421697              
2Human-Centered Artificial Intelligence, Augsburg University, Germany, ou_persistent22              
3Research Group Computational Auditory Perception, Max Planck Institute for Empirical Aesthetics, Max Planck Society, ou_3024247              

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Free keywords: Crowdsourcing, Personalization, Text/Speech/Language, Robot
 Abstract: 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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Language(s): eng - English
 Dates: 2024-05-11
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1145/3613904.3642038
 Degree: -

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Title: CHI '24: CHI Conference on Human Factors in Computing Systems
Place of Event: HI, Honolulu, USA
Start-/End Date: 2024-05-11 - 2024-05-16

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Title: CHI '24: Proceedings of the CHI Conference on Human Factors in Computing Systems
Source Genre: Proceedings
 Creator(s):
Mueller, Florian Floyd, Editor
Kyburz, Penny, Editor
Williamson, Julie R. , Editor
Sas, Corina, Editor
Wilson, Max L., Editor
Dugas, Phoebe Toups, Editor
Shklovski, Irina, Editor
Affiliations:
-
Publ. Info: -
Pages: 18961 Volume / Issue: - Sequence Number: 584 Start / End Page: 1 - 34 Identifier: ISBN: 9798400703300
DOI: 10.1145/3613904