基于特征融合的说话人聚类算法

针对单一声学特征和k-means算法在说话人聚类技术中的局限性，为了更好地表达说话人的个性信息并提高说话人聚类的准确率，将特征融合和AE-SOM神经网络应用于说话人聚类中，提出一种改进的说话人聚类算法.该算法通过对语音信号特征分析，将MFCC特征参数和LPCC特征参数相结合，从而完善说话人的个性信息.并在k-means的基础上增加AE-SOM神经网络，利用该网络实现输入特征的降维、说话人数的判定和聚类中心的选取，从而弥补k-means算法的缺陷.仿真实验表明两种声学特征融合之后，改进的聚类算法可有效地提高说话人聚类的准确率.

Speaker Clustering Algorithm Based on Feature Fusion

Aiming at the limitation of single acoustic feature and k-means algorithm in speaker clustering technology， in order to better express the speaker’s personality information and improve the accuracy of speaker clustering， feature fusion and AE-SOM neural network are applied to speaker clustering， and an improved speaker clustering algorithm is proposed. The algorithm combines MFCC feature parameters with LPCC feature parameters to improve the speaker’s personality information. The AE-SOM neural network is added on the basis of k-means to reduce the dimension of input features， determine the number of speakers and select the cluster centers， so as to make up for the defects of k-means algorithm. Simulation results show that the improved clustering algorithm can effectively improve the accuracy of speaker clustering.