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CLC number: TN912; TP391.4

On-line Access: 2014-07-10

Received: 2013-11-02

Revision Accepted: 2014-04-20

Crosschecked: 2014-06-19

Cited: 6

Clicked: 12566

Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2014 Vol.15 No.7 P.551-563


Fast global kernel fuzzy c-means clustering algorithm for consonant/vowel segmentation of speech signal

Author(s):  Xian Zang, Felipe P. Vista Iv, Kil To Chong

Affiliation(s):  Department of Electronic Engineering, Jeonbuk National University, Jeonju-si 561-756, Korea; more

Corresponding email(s):   zangxian@jbnu.ac.kr, boduke@jbnu.ac.kr, kitchong@jbnu.ac.kr

Key Words:  Fuzzy c-means clustering, Kernel method, Global optimization, Consonant/vowel segmentationAn erratum to this article can be found at doi:10.1631/jzus.C13e0320

Xian Zang, Felipe P. Vista Iv, Kil To Chong. Fast global kernel fuzzy c-means clustering algorithm for consonant/vowel segmentation of speech signal[J]. Journal of Zhejiang University Science C, 2014, 15(7): 551-563.

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journal="Journal of Zhejiang University Science C",
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%0 Journal Article
%T Fast global kernel fuzzy c-means clustering algorithm for consonant/vowel segmentation of speech signal
%A Xian Zang
%A Felipe P. Vista Iv
%A Kil To Chong
%J Journal of Zhejiang University SCIENCE C
%V 15
%N 7
%P 551-563
%@ 1869-1951
%D 2014
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300320

T1 - Fast global kernel fuzzy c-means clustering algorithm for consonant/vowel segmentation of speech signal
A1 - Xian Zang
A1 - Felipe P. Vista Iv
A1 - Kil To Chong
J0 - Journal of Zhejiang University Science C
VL - 15
IS - 7
SP - 551
EP - 563
%@ 1869-1951
Y1 - 2014
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1300320

We propose a novel clustering algorithm using fast global kernel fuzzy c-means-F (FGKFCM-F), where F refers to kernelized feature space. This algorithm proceeds in an incremental way to derive the near-optimal solution by solving all intermediate problems using kernel-based fuzzy c-means-F (KFCM-F) as a local search procedure. Due to the incremental nature and the nonlinear properties inherited from KFCM-F, this algorithm overcomes the two shortcomings of fuzzy c-means (FCM): sensitivity to initialization and inability to use nonlinear separable data. An accelerating scheme is developed to reduce the computational complexity without significantly affecting the solution quality. Experiments are carried out to test the proposed algorithm on a nonlinear artificial dataset and a real-world dataset of speech signals for consonant/vowel segmentation. Simulation results demonstrate the effectiveness of the proposed algorithm in improving clustering performance on both types of datasets.



Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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