For the inadequate training speech data of speaker identification based on short utterance

feature vectors and GMM models are optimized and improved

an efficient GMM based on local PCA with fuzzy clustering is presented.To compensate for the limited feature samples

the effective feature dimensions are increased with feature combinations instead of single feature.Furthermore

the time and space complexity of the system can be compressed by reducing dimensions of feature combinations with local fuzzy PCA in the premise of little effect on recognition rate.Finally

a new approach which combines division and fuzzy k-means clustering is used

in order to optimize GMM initialization parameters.The experiments show that the improved method is more effective in improving performance of the system than traditional initialization methods.