关键词: 层次聚类, 贝叶斯信息准则, 说话人聚类, 初始类, 语音信号处理

Abstract:  During the procedure of state-of-art speaker clustering, the individual speech segment directly obtained from speaker segmentation is used as an initial cluster, which leads to huge amount of calculation. In this paper, an algorithm of generating initial clusters for speaker clustering is thus proposed in order to reduce calculation load. First, features are extracted from speech segments, and centroids of features are calculated. Then the initial clusters are generated by clustering these centroids using both hierarchical clustering algorithm and Bayesian information criterion under an easy stopping criterion. Experiments show that doing speaker clustering on the initial clusters generated by the proposed method is faster than doing speaker clustering on the speech segments directly obtained by speaker segmentation. The computational reduction is about 40.04% without losing the performance of speaker cluster, and the computational reduction is more than 60.03% with losing little performance of speaker cluster.

Key words: hierarchical clustering, Bayesian information criterion, speaker clustering, initial clusters, speech signal processing