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Reordering Features with Weights Fusion in Multiclass and Multiple-Kernel Speech Emotion Recognition
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نویسنده
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jiang x. ,xia k. ,wang l. ,lin y.
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منبع
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journal of electrical and computer engineering - 2017 - دوره : 2017 - شماره : 0
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چکیده
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The selection of feature subset is a crucial aspect in speech emotion recognition problem. in this paper,a reordering features with weights fusion (rfwf) algorithm is proposed for selecting more effective and compact feature subset. the rfwf algorithm fuses the weights reflecting the relevance,complementarity,and redundancy between features and classes comprehensively and implements the reordering of features to construct feature subset with excellent emotional recognizability. a binary-tree structured multiple-kernel svm classifier is adopted in emotion recognition. and different feature subsets are selected in different nodes of the classifier. the highest recognition accuracy of the five emotions in berlin database is 90.549% with only 15 features selected by rfwf. the experimental results show the effectiveness of rfwf in building feature subset and the utilization of different feature subsets for specified emotions can improve the overall recognition performance. © 2017 xiaoqing jiang et al.
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آدرس
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school of electronics and information engineering,hebei university of technology,tianjin,china,school of information science and engineering,university of jinan,shandong,jinan, China, school of electronics and information engineering,hebei university of technology,tianjin, China, school of information science and engineering,university of jinan,shandong,jinan, China, school of electronics and information engineering,hebei university of technology,tianjin,china,information construction and management center,tianjin chengjian university,tianjin, China
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Authors
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