Computational modeling and eye-tracking were used to investigate how phonological and semantic information interact to influence the time course of spoken word recognition. We extended our recent models (Chen & Mirman, 2012; Mirman, Britt, & Chen, 2013) to account for new evidence that competition among phonological neighbors influences activation of semantically related concepts during spoken word recognition (Apfelbaum, Blumstein, & McMurray, 2011). The model made a novel prediction: Semantic input modulates the effect of phonological neighbors on target word processing, producing an approximately inverted-U-shaped pattern with a high phonological density advantage at an intermediate level of semantic input—in contrast to the typical disadvantage for high phonological density words in spoken word recognition. This prediction was confirmed with a new analysis of the Apfelbaum et al. data and in a visual world paradigm experiment with preview duration serving as a manipulation of strength of semantic input. These results are consistent with our previous claim that strongly active neighbors produce net inhibitory effects and weakly active neighbors produce net facilitative effects.