Voice Conversion based on GMM and Artificial Neural Network
Danwen Peng, Xiongwei Zhang, Jian Sun · 2010
Voice Conversion (VC) technique allows to transform the voice of the source speaker so that it is perceived as uttered by the target speaker. In this paper, a novel VC method combining Gaussian Mixture Model (GMM) and Artificial Neural Network is proposed. To overcome the over-smoothing problem of GMM-based mapping method, we propose to convert the basic spectral envelope by GMM method and the residual envelope by ANN method. Compared with the traditional GMM based method, the proposed method can effectively improve the quality and naturalness of the converted speech. Experimental results using both objective tests and listening tests show the superiority of the new method.