Singing Voice Synthesis Based on Generative Adversarial Networks

Yukiya Hono; Kei Hashimoto; Keiichiro Oura; Yoshihiko Nankaku; Keiichi Tokuda

ICASSP 2019accepted0 citations

Singing Voice Synthesis Based on Generative Adversarial Networks

Yukiya Hono, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, Keiichi Tokuda

Abstract

This paper proposes a generative adversarial training method for deep neural network (DNN)-based singing voice synthesis. The DNN-based approach has been used in statistical parametric singing voice synthesis and improved the naturalness of the synthesized singing voice [1]. Recently, generative adversarial networks (GANs) [2] have attracted significant attention in various machine learning research areas including speech synthesis [3]. GANs have achieved great success in modeling the distributions of complex data, and they have the potential to alleviate over-smoothing problem on the generated speech parameters in speech synthesis. In this paper, we propose a DNN-based singing voice synthesis system incorporating the GAN. Experimental results show that the proposed method outperforms the conventional method in the naturalness of the synthesized singing voice.

BibTeX

@inproceedings{icassp2019_singingvoicesynt,
  title = {Singing Voice Synthesis Based on Generative Adversarial Networks},
  author = {Yukiya Hono and Kei Hashimoto and Keiichiro Oura and Yoshihiko Nankaku and Keiichi Tokuda},
  booktitle = {ICASSP 2019},
  year = {2019}
}