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Voice Classification Algorithm for Sasang Constitution Using Support Vector Machine  

Kang, Jae-Hwan (Korea Institute of Oriental Medicine)
Do, Jun-Hyeong (Korea Institute of Oriental Medicine)
Kim, Jong-Yeol (Korea Institute of Oriental Medicine)
Publication Information
Journal of Sasang Constitutional Medicine / v.22, no.1, 2010 , pp. 17-25 More about this Journal
Abstract
1. 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. 2. 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. 3. 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. 4. Conclusions: 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.
Keywords
Voice Classifier; Support Vector Machine; SCM; TKM;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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