Bootstrapping the lexicon: a computational model of infant speech segmentation.
Prelinguistic infants must find a way to isolate meaningful chunks from the continuous streams of speech that they hear. BootLex, a new model which uses distributional cues to build a lexicon, demonstrates how much can be accomplished using this single source of information. This conceptually simple probabilistic algorithm achieves significant segmentation results on various kinds of language corpora - English, Japanese, and Spanish; child- and adult-directed speech, and written texts; and several variations in coding structure - and reveals which statistical characteristics of the input have an influence on segmentation performance. BootLex is then compared, quantitatively and qualitatively, with three other groups of computational models of the same infant segmentation process, paying particular attention to functional characteristics of the models and their similarity to human cognition. Commonalities and contrasts among the models are discussed, as well as their implications both for theories of the cognitive problem of segmentation itself, and for the general enterprise of computational cognitive modeling.
Batchelder
Eleanor Olds
EO
The Graduate Center of the City University of New York, New York, USA. eob@post.harvard.edu
D000465:Algorithms; D006801:Humans; D007223:Infant; D007802:Language; D007804:Language Development; D016013:Likelihood Functions; D008432:Mathematical Computing; D015233:Models, Statistical; D010700:Phonetics; D002666:Psychology, Child; D012660:Semantics; D013067:Speech Perception