• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >A feature selection and feature fusion combination method for speaker-independent speech emotion recognition
【24h】

A feature selection and feature fusion combination method for speaker-independent speech emotion recognition

机译：一种独立于说话人的语音情感识别的特征选择与特征融合组合方法

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

To enhance the recognition rate of speaker independent speech emotion recognition, a feature selection and feature fusion combination method based on multiple kernel learning is presented. Firstly, multiple kernel learning is used to obtain sparse feature subsets. The features selected at least n times are recombined into another subset named n-subset. The optimal n is determined by 10 cross-validation experiments. Secondly, feature fusion is made at the kernel level. Not only each kind of feature is associated with a kernel, but also the full feature set is associated with a kernel which is not considered in the previous studies. All of the kernels are added together to obtain a combination kernel. The final recognition rate for 7 kinds of emotions on Berlin Database is 83.10%, which outperforms state-of-the-art results and shows the effectiveness of our method. It is also proved that MFCCs play a crucial role in speech emotion recognition.
机译：为了提高说话人独立语音情感识别的识别率，提出了一种基于多核学习的特征选择与特征融合组合方法。首先，利用多核学习获得稀疏特征子集。至少选择了n次的特征会重新组合到另一个名为n-subset的子集中。最佳n由10个交叉验证实验确定。其次，特征融合是在内核级别进行的。不仅每种功能都与一个内核相关联，而且整个功能集也与一个内核相关联，这在以前的研究中并未考虑。将所有内核加在一起以获得组合内核。在柏林数据库中，对7种情绪的最终识别率是83.10％，优于最新结果，证明了我们方法的有效性。还证明了MFCC在语音情感识别中起着至关重要的作用。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号