  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 {\it correction}
network uses auxiliary information given by a {\it 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-the-art 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.
