Fast end-to-end non-parallel voice conversion based on speaker-adaptive neural vocoder with cycle-consistent learning-Reference-Cited by-同舟云学术

Fast end-to-end non-parallel voice conversion based on speaker-adaptive neural vocoder with cycle-consistent learning

Published:2024 Issue: Volume: Page:
ISSN:1346-3969
Container-title:Acoustical Science and Technology
language:en
Short-container-title:Acoust. Sci. & Tech.

Author:

Imai Shuhei¹,Kanagaki Aoi¹,Nose Takashi¹,Fukawa Shogo¹,Ito Akinori¹

Affiliation:

1. Graduate School of Engineering, Tohoku University

Publisher

Acoustical Society of Japan

Link

https://www.jstage.jst.go.jp/article/ast/advpub/0/advpub_e24.46/_pdf

Reference17 articles.

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2. 2) E. Casanova, J. Weber, C. D. Shulby, A. C. Junior, E. Gölge, and M. A. Ponti, “YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for Everyone,” in Proc. ICML. PMLR, 2022, pp. 2709–2720.

3. 3) S. Gao, X. Wu, C. Xiang, and D. Huang, “Development of a computationally efficient voice conversion system on mobile phones,” APSIPA Transactions on Signal and Information Processing, vol. 8, pp. 1–20, 2019.

4. 4) T. Saeki, Y. Saito, S. Takamichi, and H. Saruwatari, “Real-time, full-band, online DNN-based voice conversion system using a single CPU,” in Proc. INTERSPEECH, 2020, pp. 1021–1022.

5. 5) P. L. Tobing and T. Toda, “Low-latency real-time nonparallel voice conversion based on cyclic variational autoencoder and multiband WaveRNN with data-driven linear prediction,” in Proc. SSW, 2021, pp. 142–147.