Abstract: This paper proposes new methods of speaker verification,which use speaker model synthesis(SMS) and feature mapping based on maximum-likelihood linear regression.MAP method determines a linear relationship among the corresponding models after adjustment and transformation parameters are determined artificially,while MLLR first identify a linear relationship among the corresponding models and transformation parameters are determined from the training data,also it can only adjust the mean vectors.In SMS,MLLR determines transformation parameters among different channel UBMs.In feature mapping,MLLR determines transformation parameters between Root GMM-UBM and the channel UBM.By grouping to the model parameters,it can reach a balance between the training data and the number of parameters.The experimental results show that MLLR adjustment can achieve better verification effect than MAP adjustment by selecting the appropriate classes of regression.