Phonetic and phonological sound changes in an agent-based model

The focus of the study is an agent-based model (ABM) concerned with simulating phonological stability and change using real speech data from a population of speakers. At the core of the model was a flexible and agent-specific association between acoustic exemplars and phonological categories. This was achieved by means of two general-purpose unsupervised machine learning algorithms: the first grouped exemplars into acoustic clusters, the second identified sets of clusters which largely contain exemplars of the same word classes. The model was tested first on data from Standard Southern British English, where a shift of /u/ to the front of the vowel space was expected not to cause any phonological re-categorisation. The simulation indeed showed phonological stability despite the phonetic change. Using the same settings, the ABM was then applied to data from New Zealand English in which a merger of /e@, I@/ diphthongs has taken place in the last 50 years. Compatibly with that, the simulation showed a shift from /e@/ towards /I@/ along with the neutralisation of the diphthong contrast. The results are discussed with respect to the conditions necessary to model a merger, taking into account both findings on marginal phonological relationships and principles from exemplar theory.