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.