Voice conversion based on a mixture density network

This paper presents a new voice conversion (VC) algorithm based on a Mixture Density Network (MDN). MDN is the combination of a Gaussian Mixture Model (GMM) and an Artificial Neural Network (ANN), where the parameters of the GMM are estimated by using the ANN method instead of the Expectation Maximization (EM) algorithm. This characteristic helps the MDN estimate GMM parameters more accurately, which results in lower distortion in the converted speech. To apply the MDN to VC, we combine the MDN with Maximum Likelihood Estimation, employing a Global Variance modification (MLE-GV) method. Objective results show better performance for the proposed MDN method compared with MLE and Joint Density GMM (JDGMM) methods. Subjective experiments demonstrate that the proposed method outperforms the MLE-GV and JDGMM-GV in terms of speech quality and speaker individuality.