Unaligned training for voice conversion based on a local nonlinear principal component analysis approach

During the past years, various principal component analysis algorithms have been developed. In this paper, a new approach for local nonlinear principal component analysis is proposed which is applied to capture voice conversion (VC). A new structure of autoassociative neural network is designed which not only performs data partitioning but also extracts nonlinear principal components of the clusters. Performance of the proposed method is evaluated by means of two experiments that illustrate its efficiency; at first, performance of the network is described by means of an artificial dataset and then, the developed method is applied to perform VC.