TEXT-INDEPENDENT VOICE CONVERSION BASED ON CHINESE PHONEME CLASSIFICATION AND KERNEL EIGENVOICES GAUSSIAN MIXTURE MODEL

摘要

This paper proposed a novel algorithm for text-independent voice conversion based on Chinese phoneme classification and kernel eigenvoices Gaussian mixture model. The phoneme classification can avoid the disturbance of linguistic information and spectral smoothing. A speaker adaptation technique of kernel eigenvoices was employed for performing spectral conversion between speakers for each category phoneme, adapting the conversion parameters derived for the pre-stored pairs of speakers to a desired pair, which can relax the parallel constraint effectively. Objective test on the spectral conversion accuracy demonstrated that the proposed kernel algorithm can effectively exploit the nonlin-earity in supervector space. In subjective listening test, an ABX test was performed and the proposed algorithm was preferred to the existing eigenvoice algorithm by 4.75%, and improved quality by 10.91% in terms of mean opinion score (MOS). Both objective and subjective tests demonstrated that the proposed algorithm effectively enhanced speech quality and speaker individuality in a text-independent manner.