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We present an update to our initial work [1] on overlapped speech detection for improving speaker diarization. Specifically, we describe the addition of new features and feature warping techniques that improve segmenter and, consequently, diarization performance. We also demonstrate improved diarization performance by additionally using overlap segment information in a new diarization pre-processing step which excludes overlap segments from speaker clustering. On a subset of the AMI Meeting Corpus we show that this overlap exclusion step nearly triples the relative improvement of diarization error rate as compared to overlap segment post-processing alone.