Using Semantics to help learn Phonetic Categories – Stella Frank (University of Edinburgh)

October 17, 2013 all-day

View Seminar Video
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.
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.

Johns Hopkins University

Johns Hopkins University, Whiting School of Engineering

Center for Language and Speech Processing
Hackerman 226
3400 North Charles Street, Baltimore, MD 21218-2680

Center for Language and Speech Processing