Abstract
The correlation and redundancy of the pathological voice features, which is assorted to the feature set by the random or artificial combinations of these features, always affect the detection effect of the voice. In this paper, we present a method of optimization of pathological voice feature based on KPCA and SVM. Thus, the feature parameters are processed, the correlation and redundant information eliminated, and the representable information extracted for recognition by KPCA. Our experiments based on KPCA show that the highest recognition rate of vowel /a/ is 97.47%, the average recognition rate 91.85%, while these two rates of vowel /i/ are 91.39% and 84.15% respectively. Compared with the traditional combination method, the average recognition rate has effective improvement in our experiment based on KPCA.
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References
Huami, J., Shuping, N.: Due and counter measures of teachers voice ailments.: Journal of xinghai conservatory of Music. In: No. 2: pp. 100–103(June 2006)
Liang, C., Xiongwei, Z.: Study the nonlinear characteristics of the speech signal. Journal of PLA Science and Technology 5(7), 11–17 (2007)
Junfen, G., Weiping, H.: Recognition and study of pathological voice based on nonlinear dynamics SVM. Journal of Biomedical Engineering 29(3), 5–8 (2011)
Yingji, G., Weiping, H.: Recognition and study of pathological voice based on HHT. Computer Engineering and Applications 43(34), 217–219 (2007)
Scholkopf, B., Smola, A., Muller, K.: Nonlinear Component analysis as a Kernel eigenvalue problem. Nearal Computation 70(5), 1299–1319 (1998)
Qi, K., Kang, W., Bingyao, H.: An Kernel optimization for KPCA based on Gaussianly estimation. International Journal of Bio-Inspired Computation 6(2), 91–107 (2014)
Nayu, J.: Comparative analysis Jacobi iteration method and Gauss-seidel iterative method convergence. Journal of Yuxi Normal University 25(4) (2009)
Bingxin, Z., Weiping, H.: Recognition entropy of pathological voice based on support vector machines. Journal of Biomedical Engineering (5), 546–552 (2013)
Vapnik. V. The Nature of Statis tical Learning Theory. Springer, N. Y.
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Wang, H., Hu, W. (2014). Optimization of Pathological Voice Feature Based on KPCA and SVM. In: Sun, Z., Shan, S., Sang, H., Zhou, J., Wang, Y., Yuan, W. (eds) Biometric Recognition. CCBR 2014. Lecture Notes in Computer Science, vol 8833. Springer, Cham. https://doi.org/10.1007/978-3-319-12484-1_44
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DOI: https://doi.org/10.1007/978-3-319-12484-1_44
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12483-4
Online ISBN: 978-3-319-12484-1
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