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Cluster-Dependent Feature Transformation for Telephone-Based Speaker Verification

  • Chi-Leung Tsang
  • Man-Wai Mak
  • Sun-Yuan Kung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2688)

Abstract

This paper presents a cluster-based feature transformation technique for telephone-based speaker verification when labels of the handset types are not available during the training phase. The technique combines a cluster selector with cluster-dependent feature transformations to reduce the acoustic mismatches among different handsets. Specifically, a GMM-based cluster selector is trained to identify the cluster that best represents the handset used by a claimant. Handset distorted features are then transformed by cluster-specific feature transformation to remove the acoustic distortion before being presented to the clean speaker models. Experimental results show that cluster-dependent feature transformation with number of clusters larger than the actual number of handsets can achieve a performance level very close to that achievable by the handset-based transformation approaches.

Keywords

Gaussian Mixture Model Transformation Parameter Equal Error Rate Feature Transformation Speaker Model 
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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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Chi-Leung Tsang
    • 1
  • Man-Wai Mak
    • 1
  • Sun-Yuan Kung
    • 2
  1. 1.Center for Multimedia Signal Processing Dept. of Electronic and Information EngineeringThe Hong Kong Polytechnic UniversityHong Kong SAR, China
  2. 2.Dept. of Electrical EngineeringPrinceton UniversityUSA

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