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基于Fisher准则与SVM的分层语音情感识别

, PP. 604-609

Keywords: 语音情感识别,说话人无关,Fisher准则,支持向量机

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Abstract:

针对说话人无关的语音情感识别,提出一个分层语音情感识别模型,由粗到细识别悲伤、愤怒、惊奇、恐惧、喜悦和厌恶6种情感。每层采用Fisher比率从288个备选特征中选择适合该层分类的特征,同时将Fisher比率作为输入参数训练该层的支持向量机分类器。基于北京航空航天大学情感语音数据库和德国柏林情感语音数据库,设计4组对比实验,实验结果表明,Fisher准则在两两分类特征选择上优于PCA,SVM在说话人无关的语音情感识别推广方面优于人工神经网络(ANN)。在两个数据库的基础上得到类似结果,说明文中分类模型具有一定的跨文化适应性。

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