ISCA Archive Interspeech 2009
ISCA Archive Interspeech 2009

Adaptive individual background model for speaker verification

Yossi Bar-Yosef, Yuval Bistritz

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.


doi: 10.21437/Interspeech.2009-379

Cite as: Bar-Yosef, Y., Bistritz, Y. (2009) Adaptive individual background model for speaker verification. Proc. Interspeech 2009, 1271-1274, doi: 10.21437/Interspeech.2009-379

@inproceedings{baryosef09_interspeech,
  author={Yossi Bar-Yosef and Yuval Bistritz},
  title={{Adaptive individual background model for speaker verification}},
  year=2009,
  booktitle={Proc. Interspeech 2009},
  pages={1271--1274},
  doi={10.21437/Interspeech.2009-379}
}