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This paper presents a new approach to condition-adjusted T-norm (CT-Norm) for speaker verification under significant mismatched noise conditions. The study is motivated by the fact that, though the standard CT-Norm method offers enhanced accuracy under mismatched data conditions, its effectiveness reduces with the increased severity of such conditions. The proposed approach attempts to address this challenge by providing a more effective reduction of data mismatch through the incorporation of multi-signal-to-noise ratio (SNR) universal background models (UBMs). The effectiveness of the proposed approach is demonstrated through experiments based on examples of real-world noise. It is shown that the superiority of the approach over CT-Norm is particularly significant for such excessive levels of test data degradation considered in the study as 5 dB SNR and below. The paper provides a description of the characteristics of the proposed approach and details the experimental analysis of its effectiveness under different noise conditions.