Earticle

초록

영어

Automatic speaker verification (ASV) systems are among the biometric systems used in security and telephone-based remote control applications. Recent years have witnessed an increasing trend in research on such systems. These systems usually use high dimension feature vectors and therefore involve high complexity. However, there is a general belief that many of the features used in such systems are irrelevant and redundant. So far, many methods for feature dimension reduction in these systems have been proposed, most of which are wrapper-based and thus computationally expensive since system performance is used for feature subset evaluation. This involves system training and performance evaluation for each feature subset, which is a time consuming task. In this paper, we propose a feature selection approach based on Relieff algorithm for ASV systems using support vector machine (SVM) classifiers. This method is wrapper-based but makes use of Relieff weights in order to have a lower using of system performance. Thus this method has lower complexity compared to other wrapper-based methods, can lead to 69% feature dimension reduction and has a 1.25% of Equal Error Rate (EER) for the best case that appeared in RBF kernel of SVM. The proposed method has been compared with Genetic Algorithm (GA) and Ant Colony Optimization (ACO) methods for feature selection task. Results show that the EER, number of selected features and time complexity of the proposed method is lower than these methods for different kernels of SVM.

목차

Abstract
 1. Introduction
 2. SVM-based Speaker Verification System
  2.1. Feature Extraction
  2.2. SVM Training for Speakers
 3. Feature Selection
  3.1. Genetic Algorithm for Feature Selection
  3.2. Ant Colony Optimization for Feature Selection
 4. Proposed System
  4.1. Relief
  4.2. Proposed Feature Selection Algorithm
 5. Experimental Setup
  5.1. TIMIT Dataset
  5.2. Feature Vectors
  5.3. Parameter Settings
 6. Experimental Results
 7. Complexity of the Proposed Algorithm
 8. Conclusion and Future Work
 References

키워드

저자

• Abdolreza Rashno [ Department of Engineering, Lorestan University ]
• Hossein SadeghianNejad [ Department of Computer Engineering, Applied Science and Technology University, Yasuj ]
• Abed Heshmati [ Department of Computer Engineering and Information Technology, Payame Noor University ]

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