In a classic experiment, Saffran, Aslin, and Newport (1996) used a headturn preference procedure to show that infants can discriminate between familiar syllable sequences ("words”) and new syllable sequences ("non-words" and "part-words"). While several computational models have simulated aspects of their data and proposed that the learning of transitional probabilities could be mediated by neural-net or chunking mechanisms, none have simulated the absolute values of infants' listening times in the different experimental conditions. In this paper, we used CHREST, a model based on chunking, to simulate these listening times. The model simulated the fact that infants listened longer to novel words (non-words and part-words) than familiar words. While the times observed with the model were longer than those observed with infants, we make a novel finding with regard to phonological store trace decay. We also propose how to modify CHREST to produce data that fits closer to the human data.