针对短语音说话人辨认训练语料不充分的特点,对特征参数和GMM模型进行优化和改进,提出一种基于局部模糊PCA的GMM说话人辨认方法.该方法采用特征组合代替单一特征,以提高有效特征维数来弥补特征样本的不足,并用局部模糊PCA对组合特征进行有效降维,在对识别率影响很小的前提下,降低了系统的时空复杂度.本文还对GMM参数初始化方法进行改进,采用分裂法与模糊k均值聚类相结合方法.实验表明,与传统初始化方法相比该方法能有效提高短语音说话人辨认性能.%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 featture combinations instead of single feature. Furthermore, the time and space conmplexity 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 kmeans 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.
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