In the speaker verification (SV) task, score normalization can improve the system performance by adjusting the score distribution of each speaker to a similar distribution. Here, a large number of imposter scores for the target speakers are obtained from the development set firstly, then these scores are clustered by unsupervised clustering algorithm and the Gaussian mixture models (GMM) are used to fit the score distribution. The mean and standard deviation of Gaussian component with maximum mean value are used in the SV score normalization method. Experiments are conducted on the NIST SRE 2016 test set and results show that compared with the conventional score normalization methods, the proposed method can effectively improve the system performance.