Voice conversion based on State Space Model and considering global variance

Abstract

Voice conversion based on State Space Model (SSM) has been recently proposed to address the discontinuity problem in the traditional frame-based voice conversion by considering the spectral envelope evolutions. However, the results are over-smoothed. To resolve this problem, in this paper we propose a new procedure for integrating the global variance constraint into the SSM-based voice conversion. Moreover, unlike the SSM-based method, we allow the state-vector order to be higher than the feature-vector order. Experimental results verify that the proposed method significantly improves the performance of the SSM-based voice conversion in terms of speaker individuality and speech quality. Our experiments also show that the proposed method outperforms the well-known Maximum Likelihood estimation method that considers the Global Variance in terms of speech quality.