David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Cognitive Science 35 (1):119-155 (2011)
This paper reconsiders the diphone-based word segmentation model of Cairns, Shillcock, Chater, and Levy (1997) and Hockema (2006), previously thought to be unlearnable. A statistically principled learning model is developed using Bayes’ theorem and reasonable assumptions about infants’ implicit knowledge. The ability to recover phrase-medial word boundaries is tested using phonetic corpora derived from spontaneous interactions with children and adults. The (unsupervised and semi-supervised) learning models are shown to exhibit several crucial properties. First, only a small amount of language exposure is required to achieve the model’s ceiling performance, equivalent to between 1 day and 1 month of caregiver input. Second, the models are robust to variation, both in the free parameter and the input representation. Finally, both the learning and baseline models exhibit undersegmentation, argued to have significant ramifications for speech processing as a whole
|Keywords||Word segmentation Language acquisition Computational model Bayesian Unsupervised learning|
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References found in this work BETA
Adam Albright & Bruce Hayes (2003). Rules Vs. Analogy in English Past Tenses: A Computational/Experimental Study. Cognition 90 (2):119-161.
Eleanor Olds Batchelder (2002). Bootstrapping the Lexicon: A Computational Model of Infant Speech Segmentation. Cognition 83 (2):167-206.
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Citations of this work BETA
Andrew Martin, Sharon Peperkamp & Emmanuel Dupoux (2013). Learning Phonemes With a Proto-Lexicon. Cognitive Science 37 (1):103-124.
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