Investigating deep neural structures and their interpretability in the domain of voice conversion

Broughton, S.J., Jalal, M.A. and Moore, R.K. orcid.org/0000-0003-0065-3311 (2021) Investigating deep neural structures and their interpretability in the domain of voice conversion. In: Heřmanský, H., Černocký, H., Burget, L., Lamel, L., Scharenborg, O. and Motlicek, P., (eds.) Interspeech 2021. Interspeech 2021, 30 Aug - 03 Sep 2021, Brno, Czechia. ISCA - International Speech Communication Association , pp. 806-810.

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Copyright, Publisher and Additional Information: © 2021 ISCA. This is an author-produced version of a paper subsequently published in Interspeech 2021 Proceedings. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: voice conversion (VC); generative adversarial networks (GANs); canonical correlation analysis (CCA); SVCCA; transfer learning; non-parallel VC; multi-domain VC
Dates:
  • Published (online): 30 August 2021
  • Published: 30 August 2021
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: 25 Nov 2021 07:30
Last Modified: 25 Nov 2021 08:59
Published Version: https://www.isca-speech.org/archive/interspeech_20...
Status: Published
Publisher: ISCA - International Speech Communication Association
Refereed: Yes
Identification Number: https://doi.org/10.21437/interspeech.2021-1730
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