TY - JOUR
T1 - Speech Emotion Recognition Based on Decision Tree and Improved SVM Mixed Model
AU - Zhao, Juan Juan
AU - Ma, Rui Liang
AU - Zhang, Xiao Long
PY - 2017/4/1
Y1 - 2017/4/1
N2 - To effectively improve the accuracy of speech emotion recognition in intelligent man-machine harmonious interaction, a method of speech emotion recognition was proposed based on decision tree and an improved SVM mixed model. This method can avoid the tree unbounded generalization error, more the number of classifiers and other shortcomings, while taking advantage of SVM-KNN mixed model to avoid constrained optimization problems and improve the recognition efficiency. In this paper, six basic emotions were identified, including sadness, joy, anger, disgust, surprise, fear. Experimental results show that this method can effectively identify six basic emotions. Compared with the traditional support vector machine and artificial neural network method, this method can get higher recognition accuracy, better stability, strong practicability and generalization ability.
AB - To effectively improve the accuracy of speech emotion recognition in intelligent man-machine harmonious interaction, a method of speech emotion recognition was proposed based on decision tree and an improved SVM mixed model. This method can avoid the tree unbounded generalization error, more the number of classifiers and other shortcomings, while taking advantage of SVM-KNN mixed model to avoid constrained optimization problems and improve the recognition efficiency. In this paper, six basic emotions were identified, including sadness, joy, anger, disgust, surprise, fear. Experimental results show that this method can effectively identify six basic emotions. Compared with the traditional support vector machine and artificial neural network method, this method can get higher recognition accuracy, better stability, strong practicability and generalization ability.
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U2 - 10.15918/j.tbit1001-0645.2017.04.011
DO - 10.15918/j.tbit1001-0645.2017.04.011
M3 - Article
AN - SCOPUS:85020834750
SN - 1001-0645
VL - 37
SP - 386-390 and 395
JO - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
JF - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
IS - 4
ER -