Mixture of Factor Analyzers Using Priors from Non-Parallel Speech for Voice Conversion

• Main Authors: Zhizheng Wu, Tomi Kinnunen, Eng Siong Chng, Senior Member, Haizhou Li
• Other Authors: The Pennsylvania State University CiteSeerX Archives
• Format: Text
• Language: English
• Subjects: Arctic
• Online Access: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.304.6118; http://cs.joensuu.fi/pages/tkinnu/webpage/pdf/TMFA_IEEESPL_2012.pdf

Abstract—A robust voice conversion function relies on a large amount of parallel training data, which is difficult to collect in practice. To tackle the sparse parallel training data problem in voice conversion, this paper describes a mixture of factor analyzers method which integrates prior knowledge from nonparallel speech into the training of conversion function. The experiments on CMU ARCTIC corpus show that the proposed method improves the quality and similarity of converted speech. With both objective and subjective evaluations, we show the proposed method outperforms the baseline GMM method. Index Terms—Voice conversion, prior knowledge, factor analysis, mixture of factor analyzers. I.