A novel fuzzy-based automatic speaker clustering algorithm

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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. © 2009 Springer Berlin Heidelberg.

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Wang, H., Zhang, X., Suo, H., Zhao, Q., & Yan, Y. (2009). A novel fuzzy-based automatic speaker clustering algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5552 LNCS, pp. 639–646). https://doi.org/10.1007/978-3-642-01510-6_72

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