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Optimization of Pathological Voice Feature Based on KPCA and SVM

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Biometric Recognition (CCBR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8833))

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Abstract

The correlation and redundancy of the pathological voice features, which is assorted to the feature set by the random or artificial combinations of these features, always affect the detection effect of the voice. In this paper, we present a method of optimization of pathological voice feature based on KPCA and SVM. Thus, the feature parameters are processed, the correlation and redundant information eliminated, and the representable information extracted for recognition by KPCA. Our experiments based on KPCA show that the highest recognition rate of vowel /a/ is 97.47%, the average recognition rate 91.85%, while these two rates of vowel /i/ are 91.39% and 84.15% respectively. Compared with the traditional combination method, the average recognition rate has effective improvement in our experiment based on KPCA.

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Author information

Authors and Affiliations

  1. Guangxi Key Lab of Multi-source Information Mining & Security, Electronic Engineering College, Guangxi Normal University, Guilin, 541004, China

    Houying Wang & Weiping Hu

Authors
  1. Houying Wang
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  2. Weiping Hu
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Editor information

Editors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, P.O. Box 2728, 100190, Beijing, China

    Zhenan Sun

  2. Institute of Computing Technology, Chinese Academy of Sciences, No.6 Kexueyuan South Road, P.O. Box 2704, 100190, HaiDian, Beijing, China

    Shiguang Shan

  3. School of Information Science and Engineering, Shenyang University of Technology, No.111, Shenliao West Road, Economic and Technological Development Zone, 110870, Shenyang, China

    Haifeng Sang  & Weiqi Yuan  & 

  4. Software Engineering Institute, Tsinghua University, No. 1 Tsinghua Yuan, Haidian District, 100084, Beijing, China

    Jie Zhou

  5. School of Computer Science and Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, 100191, Beijing, China

    Yunhong Wang

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© 2014 Springer International Publishing Switzerland

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Wang, H., Hu, W. (2014). Optimization of Pathological Voice Feature Based on KPCA and SVM. In: Sun, Z., Shan, S., Sang, H., Zhou, J., Wang, Y., Yuan, W. (eds) Biometric Recognition. CCBR 2014. Lecture Notes in Computer Science, vol 8833. Springer, Cham. https://doi.org/10.1007/978-3-319-12484-1_44

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  • DOI: https://doi.org/10.1007/978-3-319-12484-1_44

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12483-4

  • Online ISBN: 978-3-319-12484-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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