Earticle

초록

영어

In order to demonstrate the effectiveness of sparse representation techniques for speaker recognition, a dictionary of feature vectors belonging to all speakers is constructed by total variability i-vectors. Each feature vector from unknown utterance is expressed as linear weighted sum of a dictionary. The weights are calculated using Block Sparse Bayesian Learning (BSBL) where the sparsest solution can be obtained. By exploiting the speech signal’s block structure and intra-block correlation, the system performance is improved. The experimental results validate that our method outperforms the baseline systems and the system using Orthogonal Matching Pursuit (OMP) algorithm on the typical corpus and realizes the identity validation function.

목차

Abstract
 1. Introduction
 2. Sparse Representation Classification
  2.1. I-Vector Feature Extraction
  2.2. Dictionary Composition
  2.3. Speaker Recognition via BSBL
 3. Experimental Evaluations
 4. Conclusions
 Acknowledgements
 References

키워드

저자

• Wei Wang [ School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China ]
• Jiqing Han [ School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China ]
• Tieran Zheng [ School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China ]
• Guibin Zheng [ School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China ]
• Mingguang Shao [ School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605