Proceedings of the Annual Meeting of the Cognitive Science Society

This paper presents a connectionist model of how representations for syllables might be learned from sequences of phones. A simple recurrent network is trained to distinguish a set of words in an artificial language, which are presented to it as sequences of phonetic feature vectors. The distributed syllable representations that are learned as a side-effect of this task are used as input to other networks. It is shown that these representations encode syllable structure in a way which permits the regeneration of the phone sequences (for production) as well as systematic phonological operations on the representations.