A statistical model for segmentation and word discovery in fluent child directed speech is presented. An incremental unsupervised learning algorithm to infer word boundaries based on this model is described. Although the algorithm is presented as an unsupervised learner, empirical results are presented showing improved performance with training size that is consistent with predictions from learning theory.