Embedding Global Optimization and Kernelization into Fuzzy C-Means Clustering for Consonant/Vowel Segmentation

Abstract:

This paper proposes a novel clustering algorithm named global kernel fuzzy-c means (GK-FCM) to segment the speech into small non-overlapping blocks for consonant/vowel segmentation. This algorithm is realized by embedding global optimization and kernelization into the classical fuzzy c-means clustering algorithm. It proceeds in an incremental way attempting to optimally add new cluster center at each stage through the kernel-based fuzzy c-means. By solving all the intermediate problems, the final near-optimal solution is determined in a deterministic way. This algorithm overcomes the well-known shortcomings of fuzzy c-means and improves the clustering accuracy. Simulation results demonstrate the effectiveness of the proposed method in consonant/vowel segmentation.

• Periodical: Applied Mechanics and Materials (Volume 419)
• Pages: 814-819
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.419.814
• Online since: October 2013
• Authors: Xian Zang, Kil To Chong
• Keywords: Consonant/Vowel Segmentation, Fuzzy C-Means, Global Optimization, Kernel

References

[1] B. Balasko, J. Abonyi, and B. Feil, Fuzzy clustering and data analysis toolbox, Department of Process Engineering, University of Veszprem, Hungary, (2005).

[2] J. C. Dunn, A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters, J. Cybern., 3 (1974) 32-57.

DOI: 10.1080/01969727308546046

[3] J. C. Bezdek, Pattern recognition with fuzzy objective function algorithms, Plenum Press, New York, (1981).

[4] D. Q. Zhang and S. C. Chen, Clustering incomplete data using kernel-based fuzzy c-means algorithm, Neural Process. Lett., 18 (2003) 155-162.

DOI: 10.1023/b:nepl.0000011135.19145.1b

[5] H. B. Shen, J. Yang, S. T. Wang, and X. J. Liu, Attribute weighted mercer kernel based fuzzy clustering algorithm for general non-spherical datasets, Soft Comput., 10 (2006) 1061-1073.

DOI: 10.1007/s00500-005-0043-5

[6] C. Yu, Y. Li, A. Liu, and J. Liu, A novel modified kernel fuzzy c-means clustering algorithm on image segementation, in: The IEEE 14th International Conference on Computational Science and Engineering (CSE 2011), 621-626, Dalian, China (2011).

DOI: 10.1109/cse.2011.109

[7] W. Wang, Y. Zhang, Y. Li, and X. Zhang, The global fuzzy c-means clustering algorithm, in: The 6th World Congress on Intelligent Control and Automation (WCICA 2006), 3604-3607, Dalian, China (2006).

DOI: 10.1109/wcica.2006.1713041

[8] L. A. Zadeh, Fuzzy sets, Inform. Control, 8 (1965) 338-353.

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

DOI: 10.1109/pgec.1965.264137

[10] S. Saitoh, Theory of reproducing kernels and its applications, Longman Scientific and Technical, Harlow, England, (1988).

[11] B. Scholkopf, A. Smola, and K. R. Muller, Nonlinear component analysis as a kernel eigenvalue problem, Neural Comput., 10 (1998) 1299-1319.

DOI: 10.1162/089976698300017467

[12] K. R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf, An introduction to kernel-based learning algorithms, IEEE T. Neural Networ., 12 (2001) 181-201.

DOI: 10.1109/72.914517

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

DOI: 10.1016/s0031-3203(02)00060-2

[14] D. J. Hermes, Vowel-onset detection, J. Acous. Soc. Am., 87 (1990) 866-873.

[15] S. R. M. Prasanna, S. V. Gangashetty, and B. Yegnanarayana, Significance of vowel onset point for speech analysis, in: Sixth Biennial Conference on Signal Processing and Communications, IISc-Bangalore, India (2001).

[16] X. Zang and K. T. Chong, Homomorphic filtered spectral peaks energy for automatic detection of vowel onset point in continuous speech, IEICE T. Inf. Syst., E96d (2013) 949-956.

DOI: 10.1587/transinf.e96.d.949

[17] J. W. Picone, Signal modeling techniques in speech recognition, P. IEEE, 81 (1993) 1215-1247.

DOI: 10.1109/5.237532