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Recent studies have shown that deep neural networks (DNNs) performsignificantly better than shallow networks and Gaussian mixture models (GMMs)on large vocabulary speech recognition tasks. In this paper, we argue that theimproved accuracy achieved by the DNNs is the result of their ability toextract discriminative internal representations that are robust to the manysources of variability in speech signals. We show that these representationsbecome increasingly insensitive to small perturbations in the input withincreasing network depth, which leads to better speech recognition performancewith deeper networks. We also show that DNNs cannot extrapolate to test samplesthat are substantially different from the training examples. If the trainingdata are sufficiently representative, however, internal features learned by theDNN are relatively stable with respect to speaker differences, bandwidthdifferences, and environment distortion. This enables DNN-based recognizers toperform as well or better than state-of-the-art systems based on GMMs orshallow networks without the need for explicit model adaptation or featurenormalization.

Feature Learning in Deep Neural Networks - Studies on Speech Recognition Tasks