Title: Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networks,
Abstract: Building a voice conversion (VC) system from non-parallel speech corpora is
challenging but highly valuable in real application scenarios. In most
situations, the source and the target speakers do not repeat the same texts or
they may even speak different languages. In this case, one possible, although
indirect, solution is to build a generative model for speech. Generative models
focus on explaining the observations with latent variables instead of learning
a pairwise transformation function, thereby bypassing the requirement of speech
frame alignment. In this paper, we propose a non-parallel VC framework with a
variational autoencoding Wasserstein generative adversarial network (VAW-GAN)
that explicitly considers a VC objective when building the speech model.
Experimental results corroborate the capability of our framework for building a
VC system from unaligned data, and demonstrate improved conversion quality.