Modular Global Variance enhancement for voice conversion systems

Voice conversion systems aim to transform sentences said by one speaker, to sound as if another speaker had said them. Many statistically trained conversion methods produce muffled synthesized outputs due to over-smoothing of the converted spectra. To deal with the muffling effect, conversion methods integrated with Global Variance (GV) enhancement, have been proposed. In order to gain the benefits of GV enhancement, the user is restricted to apply one of these methods as a conversion method. We propose a new GV enhancement method designed independently of any specific conversion scheme and applied as a post-processing block. The extent of GV enhancement is controlled through the allowed spectral distance between the enhanced and the originally converted output, as specified by the user. Listening tests showed that the proposed method improves both quality and similarity to the target of the examined converted sentences, outperforming other enhancement approaches that we evaluated.