计算机科学 ›› 2011, Vol. 38 ›› Issue (8): 212-213.

• 人工智能 • 上一篇    下一篇

面向非特定人语音情感识别的PCA特征选择方法

罗宪华,杨大利,徐明星,徐露   

  1. (北京信息科技大学计算机学院 北京100101);(清华大学计算机科学与技术系智能技术与系统国家重点实验室 清华信息科学与技术国家实验室 北京100084)
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受北京市属市管高等学校人才强教计划项目(PHR201007131)资助。

PCA Based Feature Selection Algorithm on Speaker-independent Speech Emotion Recognition

LUO Xian-hua,YANG Da-li,XU Ming-xing,XU Lu   

  • Online:2018-11-16 Published:2018-11-16

摘要: 在语音情感识别中,如何选取有效的情感特征是识别过程的重要环节。迄今为止,一些常用的特征选择算法虽然能够帮助提高识别性能,但也存在理论性不强、随机性高、计算量大的缺点。因此提出了一种基于主成分分析(PCA)的特征选择方法,亦即对原始特征集合先进行PCA变换,再利用变换矩阵分析出原始特征进行变换时各自的权重,最后根据权重的大小对原始特征进行选择。实验结果表明,选择出的特征对识别率具有较大的贡献,属于重要特征。

关键词: 情感识别,特征选择,主成分分析

Abstract: A very important part of emotion recognition is how to select effective emotional features. Until now, some feature selection algorithms, which are usually used, can help boost recognition accuracy. But some defects, such as less robustness in theory, a higher randomness, more computation, still exist. For these reasons, a new feature selection algorithm based on PCA (principal component analysis) was proposed. First the original feature set was transformed by PCA, then analyzing the weights of these features using the transforming matrix and finally, choosing the important features according to their weights. hhe experiment result shows that features, which arc selected by this method, make a high contribution to the recognition accuracy and they are important.

Key words: Emotion rccognition,Fcaturc sclcction,PCA

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