为了提高语音情感的正确识别率，提出一种基于多分类器投票组合的语音情感识别新方法。

A new method of speech emotion recognition via voting combination of multiple classifiers is proposed for improving speech emotion classification rate.

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新的多类SVM在一定程度上解决了传统投票决策方法的不可分区域问题，因此具有更好的分类性能。

The presented multi-class SVM is of better classification ability and can solve the unclassifiable region problems int.

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集成学习方法通过同时构造多个学习器，然后对各学习器的分类结果使用投票法得到分类结果。

Integrated learning method can get classification results by constructing many learners to make a vote on classification results.

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对分类器融合采用极大值法、极小值法、乘积法、均值法、中值法、投票法和各种决策模板融合方法。

The classifier fusion approaches include Maximum, Minimum, Product, Mean, Median, Major Voting fusion methods and decision template fusion methods.

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针对此问题，本研究对结合支持向量机（SVM）算法的几种常用非平衡数据分类方法进行实验比较，包括投票整合分类器和移动分类面等。

Combined with support vector machine (SVM) algorithm, several common approaches to deal with the unbalanced problem were compared, including majority voting, the moving boundary surface, etc.

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针对此问题，本研究对结合支持向量机（SVM）算法的几种常用非平衡数据分类方法进行实验比较，包括投票整合分类器和移动分类面等。

Combined with support vector machine (SVM) algorithm, several common approaches to deal with the unbalanced problem were compared, including majority voting, the moving boundary surface, etc.

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