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

voice conversion

nonlinear principal component analysis

genetic algorithm


Behrooz Makki

Chalmers, Signaler och system, Kommunikation, Antenner och Optiska Nätverk

Seyedali Seyedsalehi

Nasser Sadati

Mona Noori-Hosseini

IEEE symposium series on computational intelligence

Vol. 1 1 336-339
1-4244-0707-9 (ISBN)


Elektroteknik och elektronik



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