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886 JOURNAL OF SOFTWARE, VOL. 6, NO. 5, MAY 2011 al., “Input space versus feature space in kernel-based methods,” IEEE Trans. Neural Netw., vol.10, pp. 1000– 1017, September 1999. [10] Chua, K.S., “Efficient computations for large least square support vector machine classifiers,” Pattern Recogn. Lett., vol. 24, pp. 75–80, January 2003. [11] Vapnik, V., Statistical Learning Theory, Wiley, New York, 1998. [12] ZHANG Li, ZHOU Weida and JIAO Licheng, “Kernel clustering algorithm,” Chinese Journal of Computers, vol. 25, pp. 587–590, June 2002. [13] ZHAO Yugao and PENG Hong, “An improved fuzzy kernel clustering algorithm,” Journal of Xihua University: Natural Science Edition, vol. 26, pp. 48–50,69, March 2007. [14] Shawe-Taylor, J. and Bartlett, P.L., “structural risk minimization over data-dependent hierarchies,” IEEE Trans. Inform. Theor., vol. 44, pp. 1926–1940, September 1998. [15] Chih-Wei Hsu and Chih-Jen Lin, “A comparison of methods for multi-class support vector machines,” IEEE Trans. Neural Netw., vol. 13, pp. 415–425, March 2002. [16] Qin Yuping, Wang Xiukun, Li Xiangna and Wang Chunli, “Study on multi-class text classification algorithm based on hyper-sphere support vector machines,” Computer Engineering and Applications, vol.44, pp. 166–168, July 2008. [17] K. Crammer and Y. Singer, “On the learnability and design of output codes for multiclass problems,” Mach. Learn., vol. 47, pp. 201–233, February/March 2002. [18] BEZDEK J C., “Convergence theory for fuzzy c-Means: counterexamples and repaires,” IEEE Trans on SMC, vol. 17, pp. 873–877, September/October 1987. [19] John G. Webster, Medical instrumentation : application and design, 3 rd ed., New York : Wiley, 1998, pp. 183-232. © 2011 ACADEMY PUBLISHER Hao Liu received the B.Eng. and M.Eng. degrees in textile material and design from Tianjin Polytechnic University, Tianjin, China, in 2001 and 2004. He is currently working towards the PhD degree in textile engineering at Tianjin Polytechnic University, Tianjin, China. His research interests are data analysis and image processing in textile and clothing Engineering, smart textiles and apparel and wearable electronics. Xiaoming Tao is Chair Professor and Head of Institute of Textiles and Clothing. Prof. Tao graduated with a B.Eng. in Textile Engineering and a first class prize for undergraduate students from East China Institute of Textile Science and Technology in 1982. She gained her PhD in Textile Physics from University of New South Wales, Australia in 1987.Her research interests are fibrous materials, textile Science and technology, smart textiles and apparel and wearable electronics and photonics. Pengjun Xu is currently working towards the PhD degree in textile engineering at Donghua University, Shanghai, China. His research interests are fibrous materials, smart textiles and apparel. Guanxiong Qiu is professor of Tianjin Polytechnic University. His research interests are textile material and knitted engineering.
JOURNAL OF SOFTWARE, VOL. 6, NO. 5, MAY 2011 887 Consonant Recognition of Dysarthria Based on Wavelet Transform and Fuzzy Support Vector Machines Zhuo-ming Chen 1 1 The First Affiliated Hospital, Jinan University, Guangzhou, China Email: tchzm@21cn.com Wei-xin Ling 2 and Jian-hui Zhao 3, * School of Science, South China University of Technology, Guangzhou, China Email: 2 lingweixin@21cn.com, 3 awordman@163.com Tao-tao Yao 4 4 The First Affiliated Hospital, Jinan University, Guangzhou, China Email: selena.567.com@163.com Abstract—Consonant(in Chinese) recognition had important clinical significance in the assessment of dysarthria, while the consonants were so short and unstable that the recognition results of traditional methods were ineffective. The algorithm described in this paper extracted a new feature(DWTMFC-CT) of the consonants employing wavelet transformation, and the difference of similar consonants can be described more accurately by the feature. Then the algorithm classified consonants using multi-class fuzzy support vector machines(FSVM). In order to reduce the computation complexity caused by using the standard fuzzy support vector machines for multi-class classification, this paper proposed a algorithm based on two stages. Experimental results shown that the proposed algorithm could get better classification results while reducing the training time greatly. Index Terms—wavelets transform, fuzzy theory, support vector machines, consonant recognition I. INTRODUCTION There are a large number of pronunciation-impaired patients in China. It’s very important to assess the patient with dysarthria accurately. The methods of traditional dysarthria assessment, including Franchy Dysarthria Assessment and the dysarthria assessment method made by Zhongkang, with great subjectivity, often lead to diagnose inaccurately and incorrectly. Speech analysis is an effective assessment tool. With non-invasive and objective, this method can test a large number of patients in a short time. Speech analysis is often based on some long vowels [1-2], but practice shows that the clinical significance of consonant is more important than the long vowels’, and there is almost no objective assessment in dysarthria now. Automatic and accurate identification of the 21 categories of consonant in the Mandarin Language *Corresponding author © 2011 ACADEMY PUBLISHER doi:10.4304/jsw.6.5.887-893 is the key to objective assessment of consonant. Because of consonant’s instability, strong dynamic and short duration, so to identify them is difficult. There are two ways to improve the correct recognition rate: 1) To extract better feature parameters of consonant; 2) To choice a suitable recognition method. Feature parameters of speech are used widely, including Linear Prediction Cepstrum Coefficient (LPCC) and Mel Cepstrum Coefficient (MFCC), etc. They are assumed that speech signal is short-time stationarity, but the consonants are very unstable signal, so these parameter models have poor effect in consonant recognition [3]. It’s a good choice to extract the consonant features by employing wavelet transformation, because wavelet transformation has good localize characteristics of time domain and frequency domain. The time-frequency window can adjust according to the signal’s shape and multi-resolution analysis, so it can describe non-stable signal more precisely. HMM technology [4], Gaussian mixture model [5] and neural network [6] are used widely in the field of speech recognition, but all of them have some defects, which are hard to make up. HMM is poor in classification decisionmaking, and need to priori statistical knowledge first; Gaussian mixture model is also based on statistical theory, and need to a large number of training samples to get good recognition effect; neural network’s problems are hard to determine the network structure, local optimization and easy to over learning. Support vector machine is the important theory based on VCdimensional theory of statistical learning theory and structural risk minimizes principle. It seeks the best compromise between the Modal complexity and learning ability to obtain the best extension according to the limited sample information. It can solve small sample, nonlinear, high dimension and local minimal problems [7].
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Aims and Scope. Call for Papers and
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