Prediction-Adaption-Correction Recurrent Neural Networks for Low-Resource Language Speech Recognition

In this paper, we investigate the use of prediction-adaptation correction recurrent neural networks (PAC-RNNs) for low resource speech recognition. A PAC-RNN is comprised of a pair of neural networks in which a correction network uses auxiliary information given by a prediction network to help estimate the state probability. The information from the correction network is also used by the prediction network in a recurrent loop. Our model outperforms other state-of-theart neural networks (DNNs, LSTMs) on IARPA-Babel tasks. Moreover, transfer learning from a language that is similar to the target language can help improve performance further.