NON-PARALLEL TRAINING FOR VOICE CONVERSION BASED ON ADAPTATION METHOD

In this paper, we propose a simple and efficient non-parallel training scheme for voice conversion (VC). First, the speaker models are adapted from the background model using maximum a posteriori (MAP) technique. Then, by utilizing the parameters of adapted speaker models, the Gaussian normalization and mean transformation methods are proposed for VC, respectively. In addition, to improve the conversion performance of the proposed methods, a combination approach is further presented. Finally, objective and subjective experiments are carried out to evaluate the performance of the proposed scheme, the results demonstrate that our scheme can obtain comparable performance with the traditional GMM method based on parallel corpus.