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In previous work we found that automatic speech recognition (ASR) results on meetings show interesting patterns with respect to speaker overlaps, including a robust asymmetry in word error rates (WERs) before and after overlaps. The paradigm used allowed us to infer that these correlations are not due to crosstalk itself but to changes in how a person speaks around overlap regions. To better understand these ASR and perplexity results, we analyze speaker overlaps with respect to various factors, including collection site, speakers, dialog acts, and hot spots.