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Through the analysis of one-vs.-one, one-vs.-rest and the decision tree mechanism of binary support vector machine emotion classifiers, a method based on feature-driven hierarchical support vector machine is proposed for speech emotion recognition. For each layer, classifier used different feature parameters to drive its performance, and each emotion is(More)
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