Abstract：As providing the score of different types of data the same weight, the average likelihood ratio verification measure used in GMM-UBM vocal password system brings decline in the system performance. Based on the different distinguished capacity between data types, a method is proposed in the score domain which classifies the test data by UBM, combines the likelihood ratio score of each class to form new multi-dimension feature, and then implements speaker verification by SVM. By use of the proposed strategy, the traditional likelihood ratio test is converted into a two-class classification problem in the multi-dimension feature space. The equal error rate of the proposed system is relatively 41.25%, 33.33%, 37.49% and 26.03% less than that of text-dependent GMM-UBM system in the co-channel experiments on four telephone corpuses respectively. The improvement of performance is also demonstrated through the cross-channel experiments.
潘逸倩，魏思，戴礼荣，刘庆峰. 基于得分域多维特征分类器的声纹密码系统[J]. 模式识别与人工智能, 2012, 25(5): 755-761.
PAN Yi-Qian, WEI Si, Dai Li-Rong, LIU Qing-Feng. A Vocal Password System Based on Multi-Dimension Feature Classifier in Score Domain. , 2012, 25(5): 755-761.
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