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Speaker Identification Based on Subtractive Clustering Algorithm with Estimating Number of Clusters

机译:基于减法聚类算法的扬声器识别,估计簇数

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In this paper, we propose a new clustering algorithm that performs clustering the feature vectors for the speaker identification. Unlike typical clustering approaches, the proposed method does the clustering without the initial guesses of locations of the cluster centers and a priori information about the number of clusters. Cluster centers are obtained incrementally by adding one cluster center at a time through the subtractive clustering algorithm. The number of clusters is obtained by investigating the mutual relationship between clusters. The experimental results show the effectiveness of the proposed algorithm as compared with the conventional methods.
机译:在本文中,我们提出了一种新的聚类算法,该算法执行群集扬声器标识的特征向量。与典型的聚类方法不同,所提出的方法在没有群集中心的初始猜测的情况下进行群集,以及关于群集数量的先验信息。通过减法聚类算法一次添加一个群集中心来递增地获得群集中心。通过研究簇之间的相互关系来获得簇的数量。与传统方法相比,实验结果表明了所提出的算法的有效性。

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