NDLI: Novel inter-cluster distance measure combining GLR and ICR for improved agglomerative hierarchical speaker clustering

Content Provider	IEEE Xplore Digital Library
Author	Han, K.J. Narayanan, S.S.
Copyright Year	2008
Description	Author affiliation: Ming Hsieh Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA (Han, K.J.; Narayanan, S.S.)
Abstract	Agglomerative hierarchical clustering (AHC) has been a popular strategy for speaker clustering, due to its simple structure but acceptable level of performance. One of the main challenges in AHC that affects clustering performance is how to select the closest cluster pair for merging at every recursion. For this, generalized likelihood ratio (GLR) has been widely adopted as an inter-cluster distance measure. However, it tends to be affected by the size of the clusters considered, which could result in erroneous selection of the cluster pair to be merged during AHC. To tackle this problem, we propose a novel alternative to GLR in this paper, which is a combination of GLR and information change rate (ICR) that we recently introduced for addressing the aforementioned tendency of GLR. Experiments on various meeting speech data show that this combined measure improves clustering performance on average by around 30% (relative).
Starting Page	4373
Ending Page	4376
File Size	117408
Page Count	4
File Format	PDF
ISBN	9781424414833
ISSN	15206149
DOI	10.1109/ICASSP.2008.4518624
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-03-31
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Merging Error analysis Size measurement Electric variables measurement Speech analysis Laboratories Viterbi algorithm Distance measurement Size control Degradation information change rate (ICR) Speaker clustering agglomerative hierarchical clustering (AHC) generalized likelihood ratio (GLR)
Content Type	Text
Resource Type	Article

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