피처벡터 축소방법에 기반한 장애음성 분류 Classification of pathological and normal voice based on dimension reduction of feature vectors

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This paper suggests a method to improve the performance of the pathological/normal voice classification. The effectiveness of the mel frequency-based filter bank energies using the fisher discriminant ratio (FDR) is analyzed. And mel frequency cepstrum coefficients (MFCCs) and the feature vectors through the linear discriminant analysis (LDA) transformation of the filter bank energies (FBE) are implemented. This paper shows that the FBE LDA-based GMM is more distinct method for the pathological/normal voice classification than the MFCC-based GMM.
Publisher
대한음성학회
Issue Date
2007-05
Citation

대한음성학회 학술대회논문집

URI
http://hdl.handle.net/10203/25522
Appears in Collection
EE-Conference Papers(학술회의논문)
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