| 基于支持向量机的语音情感识别 |
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| 引用本文： | 王治平,赵力,邹采荣.基于支持向量机的语音情感识别[J].东南大学学报,2003,19(4):307-310. |
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| 作者姓名： | 王治平 赵力 邹采荣 |
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| 作者单位： | 东南大学无线电工程系，南京210096 |
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| 基金项目： | EducationRevitalizationProgramOrientedtothe 2 1stCenturyundertheChineseMinistryofEducation . |
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| 摘 要： | 针对语音情感识别特征识别问题，本利用支持向量机进行了研究．分析表明语音信号的情感特征参数在输入空间中不完全是一个线性分类的问题，使用非线性的核函数对输入空间进行映射可以有效地提高识别效率．与已有的多模式语音情感识别方式相比，利用高斯(径向基)核函数的支持向量机的识别效果优于其他已有的方法．
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| 关 键 词： | 支持向量机 语音情感识别 特征识别 核函数 |
Support vector machines for emotion recognition in Chinese speech |
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| Wang Zhiping,Zhao Li,ZOU Cairong.Support vector machines for emotion recognition in Chinese speech[J].Journal of Southeast University(English Edition),2003,19(4):307-310. |
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| Authors: | Wang Zhiping Zhao Li ZOU Cairong |
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| Abstract: | Support vector machines (SVMs) are utilized for emotion recognition in Chinese speech in this paper. Both binary class discrimination and the multi class discrimination are discussed. It proves that the emotional features construct a nonlinear problem in the input space, and SVMs based on nonlinear mapping can solve it more effectively than other linear methods. Multi class classification based on SVMs with a soft decision function is constructed to classify the four emotion situations. Compared with principal component analysis (PCA) method and modified PCA method, SVMs perform the best result in multi class discrimination by using nonlinear kernel mapping. |
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| Keywords: | speech signal emotion recognition support vector machines |
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