HOS 특징 벡터를 이용한 장애 음성 분류 성능의 향상 Performance Improvement of Classification Between Pathological and Normal Voice Using HOS Parameter

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This paper proposes a method to improve pathological and normal voice classification performance by combining multiple features such as auditory-based and higher-order features. Their performances are measured by Gaussian mixture models (GMMs) and linear discriminant analysis (LDA). The combination of multiple features proposed by the frame-based LDA method is shown to be an effective method for pathological and normal voice classification, with a 87.0% classification rate. This is a noticeable improvement of 17.72% compared to the MFCC-based GMM algorithm in terms of error reduction.
Publisher
대한음성학회
Issue Date
2008-06
Language
Korean
Citation

말소리, v.1, no.66, pp.61 - 72

ISSN
1226-1173
URI
http://hdl.handle.net/10203/15399
Appears in Collection
EE-Journal Papers(저널논문)
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