Investigation of F0 Conditioning and Fully Convolutional Networks in Variational Autoencoder Based Voice Conversion

Wen-Chin Huang, Yi-Chiao Wu, Chen-Chou Lo, Patrick Lumban Tobing, Tomoki Hayashi, Kazuhiro Kobayashi, Tomoki Toda, Yu Tsao, Hsin-Min Wang


In this work, we investigate the effectiveness of two techniques for improving variational autoencoder (VAE) based voice conversion (VC). First, we reconsider the relationship between vocoder features extracted using the high quality vocoders adopted in conventional VC systems, and hypothesize that the spectral features are in fact F0 dependent. Such hypothesis implies that during the conversion phase, the latent codes and the converted features in VAE based VC are in fact source F0 dependent. To this end, we propose to utilize the F0 as an additional input of the decoder. The model can learn to disentangle the latent code from the F0 and thus generates converted F0 dependent converted features. Second, to better capture temporal dependencies of the spectral features and the F0 pattern, we replace the frame wise conversion structure in the original VAE based VC framework with a fully convolutional network structure. Our experiments demonstrate that the degree of disentanglement as well as the naturalness of the converted speech are indeed improved.


 DOI: 10.21437/Interspeech.2019-1774

Cite as: Huang, W., Wu, Y., Lo, C., Tobing, P.L., Hayashi, T., Kobayashi, K., Toda, T., Tsao, Y., Wang, H. (2019) Investigation of F0 Conditioning and Fully Convolutional Networks in Variational Autoencoder Based Voice Conversion. Proc. Interspeech 2019, 709-713, DOI: 10.21437/Interspeech.2019-1774.


@inproceedings{Huang2019,
  author={Wen-Chin Huang and Yi-Chiao Wu and Chen-Chou Lo and Patrick Lumban Tobing and Tomoki Hayashi and Kazuhiro Kobayashi and Tomoki Toda and Yu Tsao and Hsin-Min Wang},
  title={{Investigation of F0 Conditioning and Fully Convolutional Networks in Variational Autoencoder Based Voice Conversion}},
  year=2019,
  booktitle={Proc. Interspeech 2019},
  pages={709--713},
  doi={10.21437/Interspeech.2019-1774},
  url={http://dx.doi.org/10.21437/Interspeech.2019-1774}
}