Connectionist learning to read aloud and comparison to human data

Research on connectionist mapping from written to spoken forms in natural language is presented. For this task, the more plausible Simple Recurrent Networks were used instead of static Neural Networks. The model was trained on a Dutch monosyllabic corpus. The effects of frequency, length and consistency were examined and were found similar to reported data in psycholinguistic experiments.