短语音说话人辨认的研究

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...

Research on the Speaker Identification Based on Short Utterance

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.