Title: Voice Conversion Based on Cross-Domain Features Using Variational Auto Encoders,
Abstract: An effective approach to non-parallel voice conversion (VC) is to utilize
deep neural networks (DNNs), specifically variational auto encoders (VAEs), to
model the latent structure of speech in an unsupervised manner. A previous
study has confirmed the ef- fectiveness of VAE using the STRAIGHT spectra for
VC. How- ever, VAE using other types of spectral features such as mel- cepstral
coefficients (MCCs), which are related to human per- ception and have been
widely used in VC, have not been prop- erly investigated. Instead of using one
specific type of spectral feature, it is expected that VAE may benefit from
using multi- ple types of spectral features simultaneously, thereby improving
the capability of VAE for VC. To this end, we propose a novel VAE framework
(called cross-domain VAE, CDVAE) for VC. Specifically, the proposed framework
utilizes both STRAIGHT spectra and MCCs by explicitly regularizing multiple
objectives in order to constrain the behavior of the learned encoder and de-
coder. Experimental results demonstrate that the proposed CD- VAE framework
outperforms the conventional VAE framework in terms of subjective tests.