Strategies to Improve the Robustness of Agglomerative Hierarchical Clustering Under Data Source Variation for Speaker Diarizatio

Many current state-of-the-art speaker diarization systems exploit agglomerative hierarchical clustering (AHC) as their speaker clustering strategy, due to its simple processing structure and acceptable level of performance. However, AHC is known to suffer from performance robustness under data source variation. In this paper, we address this problem. We specifically focus on the issues associated with the widely used clustering stopping method based on Bayesian information criterion (BIC) and the merging-cluster selection scheme based on generalized likelihood ratio (GLR). First, we propose a novel alternative stopping method for AHC based on information change rate (ICR). Through experiments on several meeting corpora, the proposed method is demonstrated to be more robust to data source variation than the BIC-based one. The average improvement obtained in diarization error rate (DER) by this method is 8.76% (absolute) or 35.77% (relative). We also introduce a selective AHC (SAHC) in t...

• Type: Journal
• Year: 2008
• Where: TASLP
• Authors: K. J. Han, S. Kim, S. S. Narayanan
• Keywords: Agglomerative Hierarchical Clustering, Merging-cluster Selection Scheme, Speaker Clustering Strategy, TASLP 2008