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.
Bibliographic reference. Bar-Yosef, Yossi / Bistritz, Yuval (2009): "Adaptive individual background model for speaker verification", In INTERSPEECH-2009, 1271-1274.