  Deep Neural Network (DNN) acoustic models have yielded many state-of-the-art
results in Automatic Speech Recognition (ASR) tasks. More recently, Recurrent
Neural Network (RNN) models have been shown to outperform DNNs counterparts.
However, state-of-the-art DNN and RNN models tend to be impractical to deploy
on embedded systems with limited computational capacity. Traditionally, the
approach for embedded platforms is to either train a small DNN directly, or to
train a small DNN that learns the output distribution of a large DNN. In this
paper, we utilize a state-of-the-art RNN to transfer knowledge to small DNN. We
use the RNN model to generate soft alignments and minimize the Kullback-Leibler
divergence against the small DNN. The small DNN trained on the soft RNN
alignments achieved a 3.93 WER on the Wall Street Journal (WSJ) eval92 task
compared to a baseline 4.54 WER or more than 13% relative improvement.
