  Recent studies have shown that deep neural networks (DNNs) perform
significantly better than shallow networks and Gaussian mixture models (GMMs)
on large vocabulary speech recognition tasks. In this paper, we argue that the
improved accuracy achieved by the DNNs is the result of their ability to
extract discriminative internal representations that are robust to the many
sources of variability in speech signals. We show that these representations
become increasingly insensitive to small perturbations in the input with
increasing network depth, which leads to better speech recognition performance
with deeper networks. We also show that DNNs cannot extrapolate to test samples
that are substantially different from the training examples. If the training
data are sufficiently representative, however, internal features learned by the
DNN are relatively stable with respect to speaker differences, bandwidth
differences, and environment distortion. This enables DNN-based recognizers to
perform as well or better than state-of-the-art systems based on GMMs or
shallow networks without the need for explicit model adaptation or feature
normalization.
