During language acquisition, children learn to segment speech into phonemes, syllables, morphemes, and words. We examine word segmentation specifically, and explore the possibility that children might have general purpose chunking mechanisms to perform word segmentation. The Voting Experts (VE) and Bootstrapped Voting Experts (BVE) algorithms serve as computational models of this chunking ability. VE finds chunks by searching for a particular information-theoretic signature: low internal entropy and high boundary entropy. BVE adds to VE the ability to incorporate information about word boundaries previously found by the algorithm into future segmentations. We evaluate the general chunking model on phonemically encoded corpora of child-directed speech, and show that it is consistent with empirical results in the developmental literature. We argue that it offers a parsimonious alternative to special purpose linguistic models.