Earticle

초록

영어

In speaker Identification systems both parametric and nonparametric probability modeling is used. The Gaussian model is the basic parametric model that is used and this model is the basis of other sophisticated and it can be performed in a completely text independent situation. However, it sounds efficient to speaker identification application, but it results long time processing in practice. In this paper, we propose a decision function by using vector quantization (VQ) techniques to decrease the training model for GMM in order to reduce the processing time. In our proposed modeling, we take the superiority of VQ, which is simplicity computation to distinguish between male and female speaker. Then, GMM is applied into the subgroup of speaker to get the accuracy rates. Experimental result shows that our hybrid VQ/GMM method always yielded better improvements in accuracy and bring reduce in time processing. All the experiments have been done in both direct recording speech and mobile phone speech signals.

목차

Abstract
 1. Introduction
 2. Previous Works
  2.1 Feature Extraction
 3. Vector Quantization
  3.1. Training Model Based On Clustering Technique:
 4. Mixture Model
  4.1. Gaussian Mixture Model:
 5. Code Separated GMM:
 7. Experimental Setup
 8. Results
 9. Conclusion
 10. Summary
 References

키워드

저자

• Piyush Lotia [ Associate Professor, Department of Electronics and Telecommunication, SSCET Bhilai,India ]
• Dr.M.R.Khan [ Additional Director and Principal, Govt. Engineering College, Raipur, India ]

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