A Decision-Tree-Based Online Speaker Clustering

Authors:
Wei Wang;Ping Lv;Qingwei Zhao;Yonghong Yan
Affiliations:
ThinkIT Speech Lab, Institute of Acoustics, Chinese Academy of Sciences,;ThinkIT Speech Lab, Institute of Acoustics, Chinese Academy of Sciences,;ThinkIT Speech Lab, Institute of Acoustics, Chinese Academy of Sciences,;ThinkIT Speech Lab, Institute of Acoustics, Chinese Academy of Sciences,
Venue:
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Year:
2007

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Abstract

When performing online speaker clustering, it is common to make clustering decision as soon as an audio segment is received. When the wrong decision is made, the error can propagate the posterior clustering. This paper describes a decision-tree-based online speaker clustering algorithm. Unlike typical online clustering approaches, the proposed method constructs a decision tree when an audio segment is received. A pruning strategy for candidate-elimination is also applied. Experiments indicate that the algorithm achieves good performance on both precision and speed. Finally, we discuss the relation between the performance and the width of the decision tree beam.