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Voice Conversion from Arbitrary Speakers Based on Deep Neural Networks with Adversarial Learning

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Advances in Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2017)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 82))

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Abstract

In this study, we propose a voice conversion technique from arbitrary speakers based on deep neural networks using adversarial learning, which is realized by introducing adversarial learning to the conventional voice conversion. Adversarial learning is expected to enable us more natural voice conversion by using a discriminative model which classifies input speech to natural speech or converted speech in addition to a generative model. Experiments showed that proposed method was effective to enhance global variance (GV) of mel-cepstrum but naturalness of converted speech was a little lower than speech using the conventional variance compensation technique.

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Acknowledgment

Part of this work was supported by JSPS KAKENHI Grant Number JP26280055 and JP15H02720.

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Authors and Affiliations

  1. Graduate School of Engineering, Tohoku University, Aramaki Aza Aoba 6–6–05, Aoba-ku, Sendai-shi, Miyagi, 980–8579, Japan

    Sou Miyamoto, Takashi Nose, Suzunosuke Ito, Harunori Koike, Yuya Chiba & Akinori Ito

  2. Department of Information and Communication Engineering, School of Engineering, Tokyo Institute of Technology, Nagatsuta-cho 4259, Midori-ku, Yokohama-shi, Kanagawa, 226-8502, Japan

    Suzunosuke Ito, Harunori Koike & Takahiro Shinozaki

Authors
  1. Sou Miyamoto
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  2. Takashi Nose
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  3. Suzunosuke Ito
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  4. Harunori Koike
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  5. Yuya Chiba
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  6. Akinori Ito
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  7. Takahiro Shinozaki
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Corresponding author

Correspondence to Sou Miyamoto .

Editor information

Editors and Affiliations

  1. Fujian Provincial Key Lab of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

  2. Swinburne University of Technology, Hawthorn, Victoria, Australia

    Pei-Wei Tsai

  3. Universiti Teknologi Petronas, Teronoh, Malaysia

    Junzo Watada

  4. University of Canberra, Bruce, Aust Capital Terr, Australia

    Lakhmi C. Jain

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Miyamoto, S. et al. (2018). Voice Conversion from Arbitrary Speakers Based on Deep Neural Networks with Adversarial Learning. In: Pan, JS., Tsai, PW., Watada, J., Jain, L. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. IIH-MSP 2017. Smart Innovation, Systems and Technologies, vol 82. Springer, Cham. https://doi.org/10.1007/978-3-319-63859-1_13

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  • DOI: https://doi.org/10.1007/978-3-319-63859-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63858-4

  • Online ISBN: 978-3-319-63859-1

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