Voice Conversion Using Nonlinear Principal Component Analysis
Paper i proceeding, 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

genetic algorithm

voice conversion

nonlinear principal component analysis


Behrooz Makki

Chalmers, Signaler och system, Kommunikationssystem, informationsteori och antenner, Kommunikationssystem

Seyedali Seyedsalehi

Nasser Sadati

Mona Noori Hosseini

IEEE symposium series on computational intelligence

Vol. 1 336-339


Elektroteknik och elektronik