  In this paper, we present a novel technique for a non-parallel voice
conversion (VC) with the use of cyclic variational autoencoder (CycleVAE)-based
spectral modeling. In a variational autoencoder(VAE) framework, a latent space,
usually with a Gaussian prior, is used to encode a set of input features. In a
VAE-based VC, the encoded latent features are fed into a decoder, along with
speaker-coding features, to generate estimated spectra with either the original
speaker identity (reconstructed) or another speaker identity (converted). Due
to the non-parallel modeling condition, the converted spectra can not be
directly optimized, which heavily degrades the performance of a VAE-based VC.
In this work, to overcome this problem, we propose to use CycleVAE-based
spectral model that indirectly optimizes the conversion flow by recycling the
converted features back into the system to obtain corresponding cyclic
reconstructed spectra that can be directly optimized. The cyclic flow can be
continued by using the cyclic reconstructed features as input for the next
cycle. The experimental results demonstrate the effectiveness of the proposed
CycleVAE-based VC, which yields higher accuracy of converted spectra, generates
latent features with higher correlation degree, and significantly improves the
quality and conversion accuracy of the converted speech.
