MS-EmoBoost: a novel strategy for enhancing self-supervised speech emotion representations

Song, H., Zhang, L., Gao, M. et al. (3 more authors) (2025) MS-EmoBoost: a novel strategy for enhancing self-supervised speech emotion representations. Scientific Reports, 15 (1). 21607. ISSN 2045-2322

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Item Type: Article
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© The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Keywords: Acoustics; Computer science; Electrical and electronic engineering
Dates:
  • Submitted: 29 July 2024
  • Accepted: 17 March 2025
  • Published (online): 1 July 2025
  • Published: 1 July 2025
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 08 Jul 2025 09:34
Last Modified: 08 Jul 2025 09:34
Status: Published
Publisher: Springer Science and Business Media LLC
Refereed: Yes
Identification Number: 10.1038/s41598-025-94727-2
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