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Study of a vocal feature selection method and vocal properties for discriminating four constitution types.

Kim KH, Ku B, Kang N, Kim YS, Jang JS, Kim JY - Evid Based Complement Alternat Med (2012)

Bottom Line: Further, we suggest a process to extract independent variables by eliminating explanatory variables and reducing their correlation and remove outlying data to enable reliable discriminant analysis.Moreover, the suitable division of data for analysis, according to the gender and age of subjects, is discussed.Finally, the vocal features are applied to a discriminant analysis to classify each constitution type.

View Article: PubMed Central - PubMed

Affiliation: Division of Constitutional Medicine Research, Korea Institute of Oriental Medicine, 461-24 Jeonmin-dong, Yuseong-gu, Daejeon 305-811, Republic of Korea.

ABSTRACT
The voice has been used to classify the four constitution types, and to recognize a subject's health condition by extracting meaningful physical quantities, in traditional Korean medicine. In this paper, we propose a method of selecting the reliable variables from various voice features, such as frequency derivative features, frequency band ratios, and intensity, from vowels and a sentence. Further, we suggest a process to extract independent variables by eliminating explanatory variables and reducing their correlation and remove outlying data to enable reliable discriminant analysis. Moreover, the suitable division of data for analysis, according to the gender and age of subjects, is discussed. Finally, the vocal features are applied to a discriminant analysis to classify each constitution type. This method of voice classification can be widely used in the u-Healthcare system of personalized medicine and for improving diagnostic accuracy.

No MeSH data available.


Related in: MedlinePlus

The vocal feature extraction program.
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Related In: Results  -  Collection


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fig1: The vocal feature extraction program.

Mentions: We implemented the C++ program pictured in Figure 1, combined with HTK [14] and Praat [15], to acquire the voice features. The voice features of vowels and a sentence were extracted from the voice wave file captured in the given environment. The window size for the feature extraction was 40 ms, and neighbouring windows were overlapped by 50%.


Study of a vocal feature selection method and vocal properties for discriminating four constitution types.

Kim KH, Ku B, Kang N, Kim YS, Jang JS, Kim JY - Evid Based Complement Alternat Med (2012)

The vocal feature extraction program.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3313581&req=5

fig1: The vocal feature extraction program.
Mentions: We implemented the C++ program pictured in Figure 1, combined with HTK [14] and Praat [15], to acquire the voice features. The voice features of vowels and a sentence were extracted from the voice wave file captured in the given environment. The window size for the feature extraction was 40 ms, and neighbouring windows were overlapped by 50%.

Bottom Line: Further, we suggest a process to extract independent variables by eliminating explanatory variables and reducing their correlation and remove outlying data to enable reliable discriminant analysis.Moreover, the suitable division of data for analysis, according to the gender and age of subjects, is discussed.Finally, the vocal features are applied to a discriminant analysis to classify each constitution type.

View Article: PubMed Central - PubMed

Affiliation: Division of Constitutional Medicine Research, Korea Institute of Oriental Medicine, 461-24 Jeonmin-dong, Yuseong-gu, Daejeon 305-811, Republic of Korea.

ABSTRACT
The voice has been used to classify the four constitution types, and to recognize a subject's health condition by extracting meaningful physical quantities, in traditional Korean medicine. In this paper, we propose a method of selecting the reliable variables from various voice features, such as frequency derivative features, frequency band ratios, and intensity, from vowels and a sentence. Further, we suggest a process to extract independent variables by eliminating explanatory variables and reducing their correlation and remove outlying data to enable reliable discriminant analysis. Moreover, the suitable division of data for analysis, according to the gender and age of subjects, is discussed. Finally, the vocal features are applied to a discriminant analysis to classify each constitution type. This method of voice classification can be widely used in the u-Healthcare system of personalized medicine and for improving diagnostic accuracy.

No MeSH data available.


Related in: MedlinePlus