Abstract: We used the filter bank as feature parameters when applied the confidence analysis to speaker recognition,it can improve the robustness,but the error rate was still very high.In order to further reduce the error rate of speaker recognition system,we proposed a new method called CBTM to get the confidence of each cepstral feature MFCC component based on the confidence of the filter bank.The CBTM evaluated the confidence of all MFCC components disposed by the Mel spectral subtraction through a confidence transform matrix,and reduced the impact of component with low confidence on the output probability by weighting the GMM variance to improve the robustness.The speaker identification experiments on Chinese speech corpus SUDA2002 show that the performance of the proposed method is better than traditional methods in the presence of white、pink、factory1 noise of NoiseX-92 database.