Abstract
When learning language, infants need to break
down the flow of input speech into minimal
word-like units, a process best described as unsupervised bottom-up segmentation. Proposed
strategies include several segmentation algorithms, but only cross-linguistically robust algorithms could be plausible candidates for human word learning, since infants have no initial knowledge of the ambient language. We
report on the stability in performance of 11
conceptually diverse algorithms on a selection of 8 typologically distinct languages. The
results are evidence that some segmentation
algorithms are cross-linguistically valid, thus
could be considered as potential strategies employed by all infants