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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).