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KMID : 0917520010080020061
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Journal of Speech Sciences
2001 Volume.8 No. 2 p.61 ~ p.71
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Classification of Patholigical Voice from ARS using Neural Network
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Abstract
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Speech material, which is collected from AR (Automatic Response System), was analyzed and classified into disease and non-disease state. The material include 11 different kinds of diseases. Along with AR speech, DA` (igital Audio Tape) speech is collected in parallel to give the bench mark.
To analyze speech material, analysis tools, which is developed local laboratory, are used to provide an improved and robust performance to the obtained parameters. To classify speech into disease and non-disease class, multi-layered neural network was used. Three different combinations of 3, G, 1 parameters are tested to obtain the proper network size and to find the best performance.
From the experiment, the classification rate of 92.5% was obtained.
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