Recurrent neural network with both side input context dependence for text-to-phoneme mapping

Among many neural network architectures that exist in the literature, the recurrent neural networks (RNN's) are of special interest due to their ability to deal with spatial temporal problems. However, in an earlier published paper, the authors shown that RNN's have poor performance in terms of phoneme accuracy when applied to the specific problem of converting text streams into their phonetic transcriptions. This is due to the fact that RNN's contains a weak left side context dependence between letters and the right side context dependence is not included. In this paper, we study the behavior of RNN that includes the context information between adjacent letters at the input. The results in terms of phoneme accuracy, for the RNN with both side input context dependence, multilayer perception and RNN, in the context of text-to-phoneme mapping, are shown.