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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: 6862

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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author="Xian Zang, Felipe P. Vista Iv, Kil To Chong",
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%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


[1]Bagirov, A.M., 2008. Modified global k-means algorithm for minimum sum-of-squares clustering problems. Pattern Recogn., 41(10):3192-3199.

[2]Balasko, B., Abonyi, J., Feil, B., 2005. Fuzzy Clustering and Data Analysis Toolbox. Department of Process Engineering, University of Veszprem, Veszprem.

[3]Bezdek, J.C., 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York.

[4]Bozkir, A.S., Sezer, E.A., 2013. FUAT—a fuzzy clustering analysis tool. Expert Syst. Appl., 40(3):842-849.

[5]Chiang, J.H., Hao, P.Y., 2003. A new kernel-based fuzzy clustering approach: support vector clustering with cell growing. IEEE Trans. Fuzzy Syst., 11(4):518-527.

[6]Cover, T.M., 1965. Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Trans. Electr. Comput., EC-14(3):326-334.

[7]Duda, R.O., Hart, P.E., 1973. Pattern Classification and Scene Analysis. Wiley, New York.

[8]Dunn, J.C., 1973. A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. J. Cybern., 3(3):32-57.

[9]Filippone, M., Camastra, F., Masulli, F., et al., 2008. A survey of kernel and spectral methods for clustering. Pattern Recogn., 41(1):176-190.

[10]Girolami, M., 2002. Mercer kernel-based clustering in feature space. IEEE Trans. Neur. Netw., 13(3):780-784.

[11]Gong, M., Su, L., Jia, M., et al., 2014. Fuzzy clustering with a modified MRF energy function for change detection in synthetic aperture radar images. IEEE Trans. Fuzzy Syst., 22(1):98-109.

[12]Hu, Y., Wu, D., Nucci, A., 2013. Fuzzy-clustering-based decision tree approach for large population speaker identification. IEEE Trans. Audio Speech Lang. Process., 21(4):762-774.

[13]Jain, A.K., Murty, M.N., Flynn, P.J., 1999. Data clustering: a review. ACM Comput. Surv., 31(3):264-323.

[14]Jamaati, M., Marvi, H., 2008. Performance assessment of joint feature derived from Mellin-cepstrum for vowel recognition. Int. Rev. Electr. Eng. IREE, 3(6):1077-1086.

[15]Kim, D.W., Lee, K.Y., Lee, D., et al., 2005. Evaluation of the performance of clustering algorithms in kernel-induced feature space. Pattern Recogn., 38(4):607-611.

[16]Li, Z., Tang, S., Xue, J., et al., 2001. Modified FCM clustering based on kernel mapping. Multispectral Image Processing and Pattern Recognition, International Society for Optics and Photonics, p.241-245.

[17]Likas, A., Vlassis, N., Verbeek, J.J., 2003. The global k-means clustering algorithm. Pattern Recogn., 36(2):451-461.

[18]Liu, C., Zhang, X., Li, X., et al., 2012. Gaussian kernelized fuzzy c-means with spatial information algorithm for image segmentation. J. Comput., 7(6):1511-1518.

[19]Mercer, J., 1909. Functions of positive and negative type, and their connection with the theory of integral equations. Phil. Trans. R. Soc. Lond. Ser. A, 209(441-458):415-446.

[20]Muller, K., Mika, S., Ratsch, G., et al., 2001. An introduction to kernel-based learning algorithms. IEEE Trans. Neur. Netw., 12(2):181-201.

[21]Nguyen, T.M., Wu, Q.M.J., 2013. Dynamic fuzzy clustering and its application in motion segmentation. IEEE Trans. Fuzzy Syst., 21(6):1019-1031.

[22]Picone, J.W., 1993. Signal modeling techniques in speech recognition. Proc. IEEE, 81(9):1215-1247.

[23]Shen, H.B., Yang, J., Wang, S.T., et al., 2006. Attribute weighted Mercer kernel based fuzzy clustering algorithm for general non-spherical datasets. Soft Comput., 10(11):1061-1073.

[24]Tsai, D.M., Lin, C.C., 2011. Fuzzy c-means based clustering for linearly and nonlinearly separable data. Pattern Recogn., 44(8):1750-1760.

[25]Wang, W., Zhang, Y., Li, Y., et al., 2006. The global fuzzy c-means clustering algorithm. 6th World Congress on Intelligent Control and Automation, p.3604-3607.

[26]Wu, Z., Xie, W., Yu, J., 2003. Fuzzy c-means clustering algorithm based on kernel method. Proc. 5th Int. Conf. on Computational Intelligence and Multimedia Applications, p.49-54.

[27]Xu, R., Wunsch, D., 2005. Survey of clustering algorithms. IEEE Trans. Neur. Netw., 16(3):645-678.

[28]Yang, M.S., Tsai, H.S., 2008. A Gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction. Pattern Recogn. Lett., 29(12):1713-1725.

[29]Yu, C.Y., Li, Y., Liu, A.L., et al., 2011. A novel modified kernel fuzzy c-means clustering algorithm on image segementation. IEEE 14th Int. Conf. on Computational Science and Engineering, p.621-626.

[30]Zadeh, L.A., 1965. Fuzzy sets. Inf. Contr., 8(3):338-353.

[31]Zhang, D.Q., Chen, S.C., 2002. Fuzzy clustering using kernel method. Int. Conf. on Control and Automation, p.162-163.

[32]Zhang, D.Q., Chen, S.C., 2003a. Clustering incomplete data using kernel-based fuzzy c-means algorithm. Neur. Process. Lett., 18(3):155-162.

[33]Zhang, D.Q., Chen, S.C., 2003b. Kernel-based fuzzy and possibilistic c-means clustering. Proc. Int. Conf. on Artificial Neural Network, p.122-125.

[34]Zhang, D.Q., Chen, S.C., 2004. A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artif. Intell. Med., 32(1):37-50.

[35]Zhao, F., 2013. Fuzzy clustering algorithms with self-tuning non-local spatial information for image segmentation. Neurocomputing, 106:115-125.

[36]Zhou, S., Gan, J.Q., 2004. Mercer kernel, fuzzy c-means algorithm, and prototypes of clusters. LNCS, 3177:613-618.

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