A Novel Fuzzy-Based Automatic Speaker Clustering Algorithm

Authors:
Haipeng Wang;Xiang Zhang;Hongbin Suo;Qingwei Zhao;Yonghong Yan
Affiliations:
ThinkIT Speech Lab, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China 100190;ThinkIT Speech Lab, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China 100190;ThinkIT Speech Lab, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China 100190;ThinkIT Speech Lab, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China 100190;ThinkIT Speech Lab, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China 100190
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Year:
2009

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Abstract

Fuzzy clustering has been proved successful in various fields in the recent past. In this paper, we introduce fuzzy clustering algorithms into the domain of automatic speaker clustering, and present a novel fuzzy-based hierarchical speaker clustering algorithm by applying fuzzy theory into the state-of-the-art agglomerative hierarchical clustering. This method follows a bottom-up strategy, and determines the fuzzy memberships according to a membership propagation strategy, which propagates fuzzy memberships in the iterative process of hierarchical clustering. Further analysis reveals that this method is an extension of conventional hierarchical clustering algorithm. Experiment results show that our method exhibits quite competitive performances compared to conventional k-means, fuzzy c-means and agglomerative hierarchical clustering algorithms.