| 改进的F-score算法在语音情感识别中的应用 |
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| 引用本文： | 叶吉祥,王聪慧. 改进的F-score算法在语音情感识别中的应用[J]. 计算机工程与应用, 2013, 49(16): 137-141 |
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| 作者姓名： | 叶吉祥 王聪慧 |
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| 作者单位： | 长沙理工大学 计算机与通信工程学院，长沙 410114 |
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| 基金项目： | 国家自然科学基金(No.61170199)；湖南省教育厅重点资助项目(No.11A004)。 |
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| 摘 要： | 针对F-score特征选择算法不能揭示特征间互信息而不能有效降维这一问题，应用去相关的方法对F-score进行改进，利用德语情感语音库EMO-DB，在提取语音情感特征的基础上，根据支持向量机（SVM）的分类精度选择出分类效果最佳的特征子集。与F-score特征选择算法对比，改进后的算法实现了候选特征集较大幅度的降维，选择出了有效的特征子集，同时得到了较理想的语音情感识别效果。
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| 关 键 词： | 特征选择 F-score 互信息 支持向量机 语音情感识别 |
Application of improvement of F-score algorithm in speech emotion recognition |
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| YE Jixiang , WANG Conghui. Application of improvement of F-score algorithm in speech emotion recognition[J]. Computer Engineering and Applications, 2013, 49(16): 137-141 |
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| Authors: | YE Jixiang WANG Conghui |
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| Affiliation: | Department of Computer & Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China |
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| Abstract: | For the F-score feature selection algorithm can not reveal the mutual information among features, the method of removing the redundancy is applied to improve the F-score algorithm. Using the German emotional speech database EMO-DB, based on the extraction of speech emotion features, the paper uses the classification accuracy of SVM to choose the best feature subset. Compared with the F-score method, the improved feature selection algorithm can achieve dimension reduction substantially, select an effective feature subset, and obtain an ideal speech emotion recognition accuracy. |
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| Keywords: | feature selection F-score mutual information Support Vector Machine(SVM) speech emotion recognition |
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