This integrated model allows us to examine how solving the word segmentation problem should affect infants' strategies for learning about phonetic variability and how phonetic learning can allow word segmentation to proceed in ways that mimic the idealized input used in previous models.
although the GGJ model achieves high segmentation accuracy on phonemic (nonvariable) input and makes errors that are qualitatively similar to human learners (tending to undersegment the input), its accuracy drops considerably on phonetically noisy data and it tends to oversegment rather than undersegment.