Abstract

This paper describes a novel spectral conversion method for the voice transformation. We perform spectral conversion between speakers using a Gaussian Mixture Model (GMM) on joint probability density of source and target features. A smooth spectral sequence can be estimated by applying maximum likelihood (ML) estimation using dynamic features to the GMM-based mapping. However, the degradation of the converted speech quality is still caused due to an over-smoothing of the converted spectra, which is inevitable in the conventional ML-based parameter estimation. In order to alleviate the over-smoothing, we propose an ML-based conversion taking account of the global variance of the converted parameter in each utterance. Experimental results show that the performance of the voice conversion can be improved by using the global variance information. Moreover, it is demonstrated that the proposed algorithm is more effective than spectral enhancement by postfiltering. 1.

Citations