Earticle

초록

영어

The performance of speaker verification system degrades when the test segments are utterances of short duration, therefore, we investigate the use of model representing our target speaker with his close speaker and his own speech data. We propose to create a new Speaker Model who groups close speakers (CS) achieved with two clustering algorithms in Automatic Speaker Verification A.S.V. Intra and Inter speaker’s variability are two clustering algorithm used in voice module. We compare the traditional approach which uses one specific customer model (Maximum a Posteriori Adaptation) with the Close Speaker model (Customers Families).Close Speaker Model (CSM) applied only when speaker model is weak achieves 42% of equal error rate. The results demonstrate that the log likelihood of close speakers is greater than the likelihood of client speaker. The false alarm from client and CSM are closest and we are constrained to enhance speaker model.

목차

Abstract
 1. Introduction
 2. Modeling and Speaker Characterization
  2.1 Speakers Characterization
  2.2 Modeling Speakers
  2.3 Clustering Algorithm
 3. Proposed Automatic Speaker Verification Architecture
  3.1 Training Phase
  3.2 Test Phase
 4. Experimental Results
  4.1 Database and Baseline System
  4.2 Comparative Study of Client and CSM Likelihood
  4.3 Speakers Models
  4.4 Voice Module
  4.5 Speaker Intervariability
 5. Discussions
 6. Conclusion
 References

키워드

저자

• Djellali Hayet [ Department of computer science, Badji Mokhtar University, LRS Laboratory, Badji Mokhtar University ]
• Laskri Mohamed Tayeb [ Department of computer science, Badji Mokhtar University, LRI Laboratory, Badji Mokhtar University ]

참고문헌

발행기관

간행물

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• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605