Huang, S., Chen, M., Xu, Y. et al. (2 more authors) (2021) WINVC : one-shot voice conversion with weight adaptive instance normalization. In: Pham, D.N., Theeramunkong, T., Governatori, G. and Liu, F., (eds.) PRICAI 2021: Trends in Artificial Intelligence 18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021, Hanoi, Vietnam, November 8–12, 2021, Proceedings, Part II. The 18th Pacific Rim International Conference on Artificial Intelligence (PRICAI), 08-12 Nov 2021, Hanoi, Vietnam (virtual). Springer International Publishing , pp. 559-573. ISBN 9783030893620
Abstract
This paper proposes a one-shot voice conversion (VC) solution. In many one-shot voice conversion solutions (e.g., Auto-encoder-based VC methods), performances have dramatically been improved due to instance normalization and adaptive instance normalization. However, one-shot voice conversion fluency is still lacking, and the similarity is not good enough. This paper introduces the weight adaptive instance normalization strategy to improve the naturalness and similarity of one-shot voice conversion. Experimental results prove that under the VCTK data set, the MOS score of our proposed model, weight adaptive instance normalization voice conversion (WINVC), reaches 3.97 with five scales, and the SMOS reaches 3.31 with four scales. Besides, WINVC can achieve a MOS score of 3.44 and a SMOS score of 3.11 respectively for one-shot voice conversion under a small data set of 80 speakers with 5 pieces of utterances per person.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2021 Springer Nature Switzerland AG. This is an author-produced version of a paper subsequently published in PRICAI 2021: Trends in Artificial Intelligence. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | one-shot voice conversion; generative adversarial networks (GANs); weight adaptive instance normalization |
Dates: |
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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: | 17 Jun 2022 11:43 |
Last Modified: | 18 Jun 2022 19:33 |
Status: | Published |
Publisher: | Springer International Publishing |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-030-89363-7_42 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:187594 |