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Abstract
Computational models of language acquisition seek to replicate human linguistic learning capabilities, such as an infant’s ability to identify the relevant sound categories in a language, given similar inputs. In this talk I will present some on-going work which extends a Bayesian model of phonetic categorisation (Feldman et al., 2013). The original model learns a lexicon as well as phonetic categories, incorporating the constraint that phonemes appear in word contexts. However, it has trouble separating minimal pairs (such as ‘cat’/’caught’/’kite’). The proposed extension adds further information via situational context information, a form of weak semantics or world knowledge, to disambiguate potential minimal pairs. I will present our current results and discuss potential next steps.
Biography
Stella Frank is currently a postdoc at the University of Edinburgh, from whence she received a PhD in Informatics in 2013. Her research interests lie in computational modelling of language acquisition using unsupervised Bayesian modelling techniques.