ISCA Archive ICSLP 2000
ISCA Archive ICSLP 2000

The signal reconstruction of speech by KPCA

Hui Yan, Xuegong Zhang, Yanda Li, Liqin Shen, Weibin Zhu

A new method for speech signal reconstruction is proposed by performing a nonlinear Kernel Principal Component Analysis (KPCA). By the use of kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, and reconstruct vectors mapping from input space by those dominant principal components. As the reconstructed vectors is expressed in high dimensional feature space and they could not exist pre-image in input space. For finding pre-image, we use iteration method to approximate the pre-image. The experimental results using KPCA in data reconstruction and denoising in speech signal show that it had many potential advantages comparing with PCA.


Cite as: Yan, H., Zhang, X., Li, Y., Shen, L., Zhu, W. (2000) The signal reconstruction of speech by KPCA. Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000), vol. 3, 91-93

@inproceedings{yan00_icslp,
  author={Hui Yan and Xuegong Zhang and Yanda Li and Liqin Shen and Weibin Zhu},
  title={{The signal reconstruction of speech by KPCA}},
  year=2000,
  booktitle={Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000)},
  pages={vol. 3, 91-93}
}