  Unseen data can degrade performance of deep neural net acoustic models. To
cope with unseen data, adaptation techniques are deployed. For unlabeled unseen
data, one must generate some hypothesis given an existing model, which is used
as the label for model adaptation. However, assessing the goodness of the
hypothesis can be difficult, and an erroneous hypothesis can lead to poorly
trained models. In such cases, a strategy to select data having reliable
hypothesis can ensure better model adaptation. This work proposes a
data-selection strategy for DNN model adaptation, where DNN output layer
activations are used to ascertain the goodness of a generated hypothesis. In a
DNN acoustic model, the output layer activations are used to generate target
class probabilities. Under unseen data conditions, the difference between the
most probable target and the next most probable target is decreased compared to
the same for seen data, indicating that the model may be uncertain while
generating its hypothesis. This work proposes a strategy to assess a model's
performance by analyzing the output layer activations by using a distance
measure between the most likely target and the next most likely target, which
is used for data selection for performing unsupervised adaptation.
