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KMID : 0878220100220010017
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Journal of Sasang Constitutional Medicine
2010 Volume.22 No. 1 p.17 ~ p.25
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Voice Classification Algorithm for Sasang Constitution Using Support Vector Machine
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Kang Jae-Hwan
Kim Jong-Yeol
Do Jun-Hyeong
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Abstract
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Objectives: Voice diagnosis has been used to classify individuals into the Sasang constitution in SCM(Sasang Constitution Medicine) and to recognize his/her health condition in TKM(Traditional Korean Medicine). In this paper, we purposed a new speech classification algorithm for Sasang constitution.
Methods:: This algorithm is based on the SVM(Support Vector Machine) technique, which is a classification method to classify two distinct groups by finding voluntary nonlinear boundary in vector space. It showed high performance in classification with a few numbers of trained data set. We designed for this algorithm using 3 SVM classifiers to classify into 4 groups, which are composed of 3 constitutional groups and additional indecision group.
Results: For the optimal performance, we found that 32.2% of the voice data were classified into three constitutional groups and 79.8% out of them were grouped correctly.
Conslusions:This new classification method including indecision group appears efficient compared to the standard classification algorithm which classifies only into 3 constitutional groups. We find that more thorough investigation on the voice features is required to improve the classification efficiency into Sasang constitution.
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KEYWORD
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Voice Classifier, Support Vector Machine, SCM, TKM
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