JSIP  Vol.5 No.1 , February 2014
Classification of Normal and Pathological Voice Using SVM and RBFNN
Abstract: The identification and classification of pathological voice are still a challenging area of research in speech processing. Acoustic features of speech are used mainly to discriminate normal voices from pathological voices. This paper explores and compares various classification models to find the ability of acoustic parameters in differentiating normal voices from pathological voices. An attempt is made to analyze and to discriminate pathological voice from normal voice in children using different classification methods. The classification of pathological voice from normal voice is implemented using Support Vector Machine (SVM) and Radial Basis Functional Neural Network (RBFNN). The normal and pathological voices of children are used to train and test the classifiers. A dataset is constructed by recording speech utterances of a set of Tamil phrases. The speech signal is then analyzed in order to extract the acoustic parameters such as the Signal Energy, pitch, formant frequencies, Mean Square Residual signal, Reflection coefficients, Jitter and Shimmer. In this study various acoustic features are combined to form a feature set, so as to detect voice disorders in children based on which further treatments can be prescribed by a pathologist. Hence, a successful pathological voice classification will enable an automatic non-invasive device to diagnose and analyze the voice of the patient.
Cite this paper: V. Sellam and J. Jagadeesan, "Classification of Normal and Pathological Voice Using SVM and RBFNN," Journal of Signal and Information Processing, Vol. 5 No. 1, 2014, pp. 1-7. doi: 10.4236/jsip.2014.51001.

[1]   L. Salhi, M. Talbi and A. Cherif, “Voice Disorders Identification Using Hybrid Approach: Wavelet Analysis and Multilayer Neural Networks,” World Academy of Science, Engineering and Technology, 2008.

[2]   A. R. Rabiner, “On the Use of Autocorrelation Analysis for Pitch Detection,” IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 25, No. 1, 1977, pp. 24-33.

[3]   C. Pend, Q. J. Xu, B. K. Wan and W. X. Chen, “Pathological Voice Classification Based on Features Dimension Optimization,” Transactions of Tianjin University, Vol. 13, No. 6, 2007.

[4]   P. Dhanalakshmi, S. Palanivel and V. Ramalingam, “Clas- sification of Audio Signals Using SVM and RBFNN,” Expert Systems with Applications, Vol. 36, No. 3, 2009, pp. 6069-6075.

[5]   E. Vaiciukynas, A. Gelzins, M. Bacauskiene, A. Verikas and A. Vegiene, “Exploring Kernels in SVM-Based Clas- sification of Larynx Pathology from Human Voice,” Department of Electrical and Control Instrumentation, Kaunas University of Technology, Lithuania.

[6]   D. Pravena, S. Dhivya and A. Durga Devi, “Pathological Voice Recognition for Vocal Fold Didease,” Interna- tional Journal of Computer Applications (0975-888), Vol. 47, No. 13, 2012.

[7]   M. de Oliviera Rosa, J. C. Pereira and M. Grellet, “Adaptive Estimation of Residue Signal for Voice Pathology Diagnosis,” IEEE Transactions on Biomedical Engineer- ing, Vol. 47, No. 1, 2000.

[8]   M. Farra, J. Hernando and P. Ejarque, “Jitter and Shimmer Measurements for Speaker Recognition,” TALP Research Center, Department of Signal Theory and Communica- tions, Universitat Politecnica de Catalunya, Barcelona.

[9]   L. R. Rabiner and R. W. Schafer, “Digital Processing of Speech Signals”.