Speaker Verification with Model-based and Score-based Unsupervised Adaptation Method
采用模型和得分非监督自适应的说话人识别

In the text-independent speaker verification research, the information of previous trials can be adopted to update the speaker models or the test scores dynamically. This process is defined as the unsupervised mode, which can make a coupling between the trials and the speaker models. The unsupervised mode is very useful for real speaker recognition application. In this paper, a score-based unsupervised adaptation is proposed as well as model-based unsupervised adaptation. In the score-based unsupervised adaptation mode, a bi-Gaussian model is introduced as a prior score distribution. Then the MAP (maximum a posteriori) method is adopted to adjust the parameters of the score normalization. In the test process, the unsupervised score adaptation and unsupervised model adaptation can both improve the performance. In the case of NIST\ SRE 2006 1conv4w-1conv4w corpus, the equal error rate (EER) of the proposed system is 4.3% and the minimum detection cost function (minDCF) is 0.021.