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The automated detection of conflict will be a crucial feature of emerging speech-analysis technologies, whether the purpose is to assuage conflict in online applications or simply to mark its location for corpus analysis. In this study, we examine the predictive potential of overlapping speech in determining conflict, and we find that this feature alone is strongly correlated with high conflict levels as rated by human judges. In analyzing the SSPNET debate corpus, we effect a 2.3% improvement over baseline accuracy using speaker overlap ratio as a predicted value, suggesting that this feature is a reliable proxy for conflict level. In a follow-up experiment, we analyze the patterns of predicted conflict in the beginning, middle and end of an audio clip. Our findings show that the beginning and final segments are more predictive than the middle, which indicates that a primacy-recency effect is bearing on the perception of conflict. Since the beginning segment itself can be quite predictive, we also show that accurate predictions can be made dynamically, allowing for real-time classification during live debates.