Exploring implicit score normalization techniques in speaker verification

In this paper we first introduce four kinds of modification of Symmetric Scoring which produce likelihood ratios that do not need to be explicitly normalized, i.e. T-norm, Z-norm. To solve the numerical problem caused by large covariance matrix calculation, we propose three solutions and present the result for each of them according to different modifications. Then we introduce a new kernel function that contains the effect of score normalization for SVM-based Speaker Verification system. We also show that these methods consistently improve the performance of the original system by means of implicit score normalization. In order to achieve more efficient computation, we evaluate an attempt to explore implicit score normalization in the much lower dimensional speaker factor space. We evaluate the performance of the proposed algorithms on the core condition of the NIST SRE 2006 dataset.