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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    Titel :

    Voice conversion using nonlinear principal component analysis


    Beteiligte:
    Makki, B. (Autor:in) / Seyedsalehi, S.A. (Autor:in) / Sadati, N. (Autor:in) / Hosseini, M. Noori (Autor:in)


    Erscheinungsdatum :

    2007-04-01


    Format / Umfang :

    4809241 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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