Adaptive individual background model for speaker verification

Yossi Bar-Yosef*, Yuval Bistritz

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

Most techniques for speaker verification today use Gaussian Mixture Models (GMMs) and make the decision by comparing the likelihood of the speaker model to the likelihood of a universal background model (UBM). The paper proposes to replace the UBM by an individual background model (IBM) that is generated for each speaker. The IBM is created using the K-nearest cohort models and the UBM by a simple new adaptation algorithm. The new GMM-IBM speaker verification system can also be combined with various score normalization techniques that have been proposed to increase the robustness of the GMM-UBM system. Comparative experiments were held on the NIST-2004-SRE database with a plain system setting (without score normalization) and also with the combination of adaptive test normalization (ATnorm). Results indicated that the proposed GMM-IBM system outperforms a comparable GMM-UBM system.

Original languageEnglish
Pages (from-to)1271-1274
Number of pages4
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
StatePublished - 2009
Event10th Annual Conference of the International Speech Communication Association, INTERSPEECH 2009 - Brighton, United Kingdom
Duration: 6 Sep 200910 Sep 2009

Keywords

  • Cohort selection
  • Gaussian mixture models
  • Kullback-Leibler divergence
  • Model adaptation
  • Score normalization
  • Speaker verification

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