Seminar: Emmanuel Dupoux
A computational approach to early language bootstrapping
May 18, 2012
from 11:00 AM to 12:30 PM
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Human infants learn spontaneously and effortlessly the language(s) spoken in their environments, despite the extraordinary complexity of the task. During the first year of life, before they are able to talk, they construct a detailed representation of the phonemes of their native language and loose the ability to distinguish nonnative phonemic contrasts (Werker & Tees, 1984). We show that the only mechanism that has been proposed so far, that is, unsupervised statistical clustering (Maye, Werker and Gerken, 2002), may not converge on the inventory of phonemes, but rather on contextual allophonic units that are smaller than the phoneme (Varadarajan, 2008). Alternative algorithms will be presented using three sources of information: the statistical distribution of their contexts, the phonetic plausibility of the grouping, and the existence of lexical minimal pairs (Peperkamp et al., 2006; Martin et al, submitted). It is shown that each of the three sources of information can be acquired without presupposing the others, but that they need to be combined to arrive at good performance. Modeling results and experiments in human infants will be presented.
Professor at the Ecole des Hautes Etudes en Sciences Sociales, Paris
1998-2009 Director of Laboratoire de Sciences Cognitives et Psycholinguistique (LSCP), CNRS, Paris.
1992 Diploma in Telecom Engineering at Télécom Paris.
1989-1990 Post-doc at the Cognitive Science Program, Univ. of Arizona.
1989 PhD in Cognitive Psychology, EHESS, Paris (J. Mehler).
1984-1988 Student at École Normale Supérieure