  State-of-the-art speech recognition systems typically employ neural network
acoustic models. However, compared to Gaussian mixture models, deep neural
network (DNN) based acoustic models often have many more model parameters,
making it challenging for them to be deployed on resource-constrained
platforms, such as mobile devices. In this paper, we study the application of
the recently proposed highway deep neural network (HDNN) for training
small-footprint acoustic models. HDNNs are a depth-gated feedforward neural
network, which include two types of gate functions to facilitate the
information flow through different layers. Our study demonstrates that HDNNs
are more compact than regular DNNs for acoustic modeling, i.e., they can
achieve comparable recognition accuracy with many fewer model parameters.
Furthermore, HDNNs are more controllable than DNNs: the gate functions of an
HDNN can control the behavior of the whole network using a very small number of
model parameters. Finally, we show that HDNNs are more adaptable than DNNs. For
example, simply updating the gate functions using adaptation data can result in
considerable gains in accuracy. We demonstrate these aspects by experiments
using the publicly available AMI corpus, which has around 80 hours of training
data.
