Modelling the emergence of speech sound categories in evolving connectionist systems

Abstract

We report on the clustering of nodes in internally represented acoustic space. Learners of different languages partition perceptual space distinctly. Here, an Evolving Connectionist-Based System (ECOS) is used to model the perceptual space of New Zealand English. Currently, the system evolves in an unsupervised, self-organising manner. The perceptual space can be visualised, and the important features of the input patterns analysed. Additionally, the path of the internal representations can be seen. The results here will be used to develop a supervised system that can be used for speech recognition based on the evolved, internal sub-word units.