This thesis adopts GMM-UBM when model speaker recognition system considering of lacking data. In the aspect of adapting in speaker recognition system modeling and parameter estimating, attentions are put on researching in how to improve recognition rate. In the side of adapting in speaker recognition system modeling, we will ameliorate conventional MAP (Maximum A Posterior Probability) means to get speaker recognition model, apply MLLR (Maximum Likelihood Linear Regression) and EigenVoice adaptation ways which used in speech recognition into adapting in speaker recognition system modeling, and compare the results with MAP means.