A Comparative Study of Feature and Score Normalization for Speaker Verification

  • Rong Zheng
  • Shuwu Zhang
  • Bo Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)


In speaker verification, it is necessary to reduce the influence of different environmental conditions. In this paper, two stages of normalization techniques, feature normalization and score normalization, are examined for decreasing the mismatch between training and testing acoustic conditions. At the first stage, cepstral mean and variance normalization (CMVN) is modified to normalize the cepstral coefficients with the similar segmental parameter statistics. Next, due to score variability between verification trials, Test-dependent zero-score normalization (TZnorm) and Zero-dependent test-score normalization (ZTnorm) are comparatively presented to transform the output scores entirely and make the speaker-independent decision threshold more robust under adverse conditions. Experiments on NIST2002 SRE corpus show that the normalizations with CMVN in feature stage and ZTnorm in score stage achieved 20.3% relative reduction of EER and 18.1% relative reduction of the minimal DCF compared to the baseline system using CMN and zero normalization.


Equal Error Rate Speaker Verification Test Segment Universal Background Model Target Speaker 
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 2005

Authors and Affiliations

  • Rong Zheng
    • 1
  • Shuwu Zhang
    • 1
  • Bo Xu
    • 1
  1. 1.Institute of AutomationChinese Academy of SciencesBeijingChina

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