Effective speaker verification via dynamic mismatch compensation

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, whilst 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-SNR UBMs (universal background models). 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 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.