Learning word pronunciations using a recurrent neural network

Abstract

Segmentation is the process of dividing a printed character string into graphemes, each of which is associated with one (or rarely more) output phonemes. The purpose of this study was to investigate what internal representation of the segmentation process and character-to-phoneme correspondences would be learned by a recurrent neural network as it was trained to produce the correct temporal sequence of phonemes for printed words held fixed on its input nodes. The resilient recurrent backpropagation network learned very effectively to generate the correct pronunciation for 150 words. Some interesting rules of pronunciation discovered by the network were extracted despite the network's distributed representation.