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

• Published in: Frontiers of information technology & electronic engineering 2014-07, Vol.15 (7), p.551-563
• Main Authors: Zang, Xian, Vista, Felipe P., Chong, Kil To
• Format: Article
• Language: English
• Subjects: Algorithms; Clustering; Communications Engineering; Computer Hardware; Computer Science; Computer Systems Organization and Communication Networks; Consonants (speech); Datasets; Electrical Engineering; Electronics and Microelectronics; Instrumentation; Networks; Segmentation; Vowels
• ISSN: 1869-1951; 2095-9184; 1869-196X; 2095-9230
• 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）： sen- sitivity to initialization and inability to use nonlinear separable data. An accelerating scheme is developed to reduce the compu-tational 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.