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A Robust Framework for High-Quality Voice Conversion with Conditional Generative Adversarial Network

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Artificial Intelligence and Security (ICAIS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1252))

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Abstract

The deep neural network (DNNs) has been applied in voice conversion (VC) system successfully. DNN shows its effectiveness especially with a large scale of training speech data, but on a small dataset, DNN suffers from the overfitting problem, resulting in performance degradation. Recently, many generative adversarial network (GAN) based VC methods have been developed, which prove to be a promising approach. However, the quality and similarity of generated speech are not entirely satisfactory. The converted speech usually consists of vague contents with buzzy noise and sounds robotic. In this paper, we study and make modification to the basic conditional generative network (cGAN). Focusing on establishing a robust framework for high-quality VC, the proposed method uses a cGAN with a L1 loss and a less explicit constraint, an identity loss. These constraints cooperate to generate speech of higher quality and similarity. We test our method on non-parallel datasets. An objective evaluation demonstrates that this approach achieves better performance and robustness than other GAN-based methods on the intra-gender task. A subjective evaluation shows that the naturalness and similarity are improved.

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Acknowledgements

This research is supported in part by the National Science and Technology Major Project for IND (investigational new drug) (Project No. 2018ZX09201-014), National Key Research and Development Project (Grant No. 2017YFC0820504), and the CETC Joint Advanced Research Foundation (Grant No. 6141B08080101, 6141B08010102).

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Authors and Affiliations

  1. Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100054, China

    Liyang Chen, Yingxue Wang & Haiyong Xie

  2. National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data (NEL-PSRPC), Beijing, China

    Yingxue Wang, Yifeng Liu & Haiyong Xie

  3. University of Science and Technology Beijing, Beijing, China

    Liyang Chen & Wendong Xiao

Authors
  1. Liyang Chen
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  2. Yingxue Wang
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  3. Yifeng Liu
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  4. Wendong Xiao
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  5. Haiyong Xie
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Corresponding author

Correspondence to Yingxue Wang .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Jinwei Wang

  3. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Chen, L., Wang, Y., Liu, Y., Xiao, W., Xie, H. (2020). A Robust Framework for High-Quality Voice Conversion with Conditional Generative Adversarial Network. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Communications in Computer and Information Science, vol 1252. Springer, Singapore. https://doi.org/10.1007/978-981-15-8083-3_18

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  • DOI: https://doi.org/10.1007/978-981-15-8083-3_18

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-8082-6

  • Online ISBN: 978-981-15-8083-3

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