Integrating Complementary Features with a Confidence Measure for Speaker Identification

  • Nengheng Zheng
  • P. C. Ching
  • Ning Wang
  • Tan Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4274)


This paper investigates the effectiveness of integrating complementary acoustic features for improved speaker identification performance. The complementary contributions of two acoustic features, i.e. the conventional vocal tract related features MFCC and the recently proposed vocal source related features WOCOR, for speaker identification are studied. An integrating system, which performs a score level fusion of MFCC and WOCOR with a confidence measure as the weighting parameter, is proposed to take full advantage of the complementarity between the two features. The confidence measure is derived based on the speaker discrimination powers of MFCC and WOCOR in each individual identification trial so as to give more weight to the one with higher confidence in speaker discrimination. Experiments show that information fusion with such a confidence measure based varying weight outperforms that with a pre-trained fixed weight in speaker identification.


Gaussian Mixture Model Speaker Recognition Speaker Identification Discrimination Ratio Complementary Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Nengheng Zheng
    • 1
  • P. C. Ching
    • 1
  • Ning Wang
    • 1
  • Tan Lee
    • 1
  1. 1.Department of Electronic EngineeringThe Chinese University of Hong KongHong Kong

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