Any-to-Any Voice Conversion With Multi-Layer Speaker Adaptation and Content Supervision-Reference-Cited by-同舟云学术

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Any-to-Any Voice Conversion With Multi-Layer Speaker Adaptation and Content Supervision

Published:2023 Issue: Volume:31 Page:3431-3445
ISSN:2329-9290
Container-title:IEEE/ACM Transactions on Audio, Speech, and Language Processing
language:
Short-container-title:IEEE/ACM Trans. Audio Speech Lang. Process.

Author:

Xu Xuexin¹^ORCID,Shi Liang¹^ORCID,Chen Xunquan²^ORCID,Lin Pingyuan¹^ORCID,Lian Jie¹^ORCID,Chen Jinhui³^ORCID,Zhang Zhihong¹^ORCID,Hancock Edwin R.⁴^ORCID

Affiliation:

1. School of Informatics, Xiamen University, Xiamen, China

2. Graduate School of System Informatics, Kobe University, Kobe, Japan

3. Faculty of Systems Engineering, Wakayama University, Wakayama, Japan

4. Department of Computer Science, University of York, York, U.K.

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

JSPS KAKENHI

Research Support Fund of Wakayama University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Acoustics and Ultrasonics,Computer Science (miscellaneous),Computational Mathematics

Link

http://xplorestaging.ieee.org/ielx7/6570655/9970249/10224343.pdf?arnumber=10224343

Reference58 articles.

1. VQVC+: One-Shot Voice Conversion by Vector Quantization and U-Net Architecture

2. AudioLM: A Language Modeling Approach to Audio Generation

3. Autovc: Zero-shot voice style transfer with only autoencoder loss;qian;Proc Int Conf Mach Learn,0

4. Visualizing data using t-sne;maaten;J Mach Learn Res,2008

5. Non-Parallel Sequence-to-Sequence Voice Conversion With Disentangled Linguistic and Speaker Representations

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