Voice conversion using nonlinear principal component analysis
Behrooz Makki, Seyedali Seyedsalehi, Nasser Sadati, Mojtaba Hosseini · 2007
In the last decades, much attention has been paid to the design of multi-speaker voice conversion. In this work, a new method for voice conversion (VC) using nonlinear principal component analysis (NLPCA) is presented. The principal components are extracted and transformed by a feed-forward neural network which is trained by combination of genetic algorithm (GA) and back-propagation (BP). Common pre- and post-processing approaches are applied to increase the quality of the synthesized speech. The results indicate that the proposed method can be considered as a step towards multi-speaker voice conversion