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Voice conversion using nonlinear principal component analysis

Makki, B ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1109/CIISP.2007.369191
  3. Publisher: 2007
  4. Abstract:
  5. 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. © 2007 IEEE
  6. Keywords:
  7. Backpropagation algorithms ; Feature extraction ; Feedforward neural networks ; Genetic algorithms ; Nonlinear analysis ; Principal component analysis ; Nonlinear principal component analysis (NLPCA) ; Principal component extraction ; Voice conversion (VC) ; Speech synthesis
  8. Source: 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007, Honolulu, HI, 1 April 2007 through 5 April 2007 ; 2007 , Pages 336-339 ; 1424407079 (ISBN); 9781424407071 (ISBN)
  9. URL: https://ieeexplore.ieee.org/abstract/document/4221441