基于蚁群算法特征选择的语音情感识别

摘要

情感特征提取是语音情感准确识别的关键,传统方法采用单一特征或者简单组合特征提取方法,单一特征无法全面反映语音情感变化,简单组合特征会使特征间产生大量冗余特征,影响识别正确结果.为了提高语音情感识别率,提了一种蚁群算法的语音情感智能识别方法.首先采用语音识别正确率和特征子集维数加权作为目标函数,然后利用蚁群算法找到最优语音特征子集,消除特征冗余信息.通过汉话和丹麦语两种情感语音库进行仿真测试,仿真结果表明,改进方法不仅消除了冗余、无用特征,降低了特征维数,而且提高了语音情感识别率,是一种有效的语音情感智能识别方法.%Speech emotion information has the characteristics of high dimension and redundancy, in order to improve the accuracy of speech emotion recognition, this paper put forward a speech emotion recognition model to select features based on ant colony optimization algorithm. The classification accuracy of KNN and the selected feature dimension form the fitness function, and the ant colony optimization algorithm provides good global searching capability and multiple sub - optimal solutions. A local refinement searching scheme was designed to exclude the redundant features and improve the convergence rate. The performance of method was tested by Chinese emotional speech database and a Danish Emotional Speech. The simulation results show that the proposed method can not only eliminate redundant and useless features to reduce the dimension of features, but also improve the speech emotion recognition rate, therefore the proposed model is an effective speech emotion recognition method.