Modeling Human Performance in Statistical Word Segmentation
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@MISC{Frank_modelinghuman,
author = {Michael C. Frank and Sharon Goldwater and Vikash Mansinghka and Tom Griffiths and Joshua Tenenbaum},
title = {Modeling Human Performance in Statistical Word Segmentation},
year = {}
}
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Abstract
What mechanisms support the ability of human infants, adults, and other primates to identify words from fluent speech using distributional regularities? In order to better characterize this ability, we collected data from adults in an artificial language segmentation task similar to Saffran, Newport, and Aslin (1996) in which the length of sentences was systematically varied between groups of participants. We then compared the fit of a variety of computational models— including simple statistical models of transitional probability and mutual information, a clustering model based on mutual information by Swingley (2005), PARSER (Perruchet & Vintner, 1998), and a Bayesian model. We found that while all models were able to successfully complete the task, fit to the human data varied considerably, with the Bayesian model achieving the highest correlation with our results.
