Earticle

초록

영어

Feature dimension reduction is important for speech emotion recognition. The classical linear discriminant analysis has been used widely in this field, but the best projection separating class from others can’t be obtained with the linear discriminant analysis method due to outlier class. To approach this problem, a novel distance weighted function based on the linear discriminant analysis is introduced, which can improve the separability of sample data and has low computational complexity. In order to evaluate the proposed algorithm’s performance, some experiments are performed on two speech databases: UCI and CASIA. Experimental results on the UCI database demonstrate that the presented algorithm has a better performance. Experimental results on CASIA show that the proposed algorithm yields an average accuracy of 88.78% in the classification of four emotions, revealing that it is a better choice as feature dimension reduction for emotion classification than the traditional algorithms.

목차

Abstract
 1. Introduction
 2. Linear Discriminant Analysis
 3. Proposed Method
 4. Experimental Results and Analysis
  4.1. Experiment on UCI Dataset
  4.2. Experiment on CASIA Chinese Emotional Speech Database
 5. Conclusion
 References

키워드

저자

• Jingjing Yuan [ School of Information Science and Technology,Northwest University, Xi’an , 710127, China ]
• Li Chen [ School of Information Science and Technology,Northwest University, Xi’an , 710127, China ]
• Taiting Fan [ Xi’an productivity promotion center, Xi’an, 710048, China ]
• Jian Jia [ Department of Mathematics, Northwest University, Xi’an, 710127, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605