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.


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    Title :

    Voice conversion using nonlinear principal component analysis


    Contributors:


    Publication date :

    2007-04-01


    Size :

    4809241 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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