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- W2372898227 abstract "To improve the accuracy of speech emotion recognition,a multi-class classifier with binarytree structure is adopted,which includes building the multi-kernel support vector machine( SVM) classifier model solved by semi-definite programming method,and using the root mean square error and maximum error to evaluate the performance of the classifier. Through the test on the parameter set obtained by feature selection algorithm,the results of experiments show that the total recognition accuracy of the proposed multiple-kernel SVM classifier model using semi-definite programming is 88. 614%,which is12. 376% higher than that of single-kernel SVM model. Moreover the multiple-kernel SVM model can reduce the total error accumulation and confusion between emotion states." @default.
- W2372898227 created "2016-06-24" @default.
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- W2372898227 date "2015-01-01" @default.
- W2372898227 modified "2023-09-28" @default.
- W2372898227 title "Speech Emotion Recognition Using Semi-Definite Programming Multiple-Kernel SVM" @default.
- W2372898227 hasPublicationYear "2015" @default.
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