Recurrent Neural-Network Learning of Phonological Regularities in Turkish

Simple recurrent networks were trained with sequences of phonemes from a corpus of Turkish words. The network's task was to predict the next phoneme. The aim of the study was to look at the representations developed within the hidden layer of the network in order to investigate the extent to which such networks can learn phonological regularities from such input. It was found that in the different networks, hidden units came to correspond to detectors for natural phonological classes such as vowels, consonants, voiced stops, and front and back vowels. The initial state of the networks contained no information of this type, nor were these classes explicit in the input. The networks were also able to encode information about the temporal distribution of these classes.