  Unseen data conditions can inflict serious performance degradation on systems
relying on supervised machine learning algorithms. Because data can often be
unseen, and because traditional machine learning algorithms are trained in a
supervised manner, unsupervised adaptation techniques must be used to adapt the
model to the unseen data conditions. However, unsupervised adaptation is often
challenging, as one must generate some hypothesis given a model and then use
that hypothesis to bootstrap the model to the unseen data conditions.
Unfortunately, reliability of such hypotheses is often poor, given the mismatch
between the training and testing datasets. In such cases, a model hypothesis
confidence measure enables performing data selection for the model adaptation.
Underlying this approach is the fact that for unseen data conditions, data
variability is introduced to the model, which the model propagates to its
output decision, impacting decision reliability. In a fully connected network,
this data variability is propagated as distortions from one layer to the next.
This work aims to estimate the propagation of such distortion in the form of
network activation entropy, which is measured over a short- time running window
on the activation from each neuron of a given hidden layer, and these
measurements are then used to compute summary entropy. This work demonstrates
that such an entropy measure can help to select data for unsupervised model
adaptation, resulting in performance gains in speech recognition tasks. Results
from standard benchmark speech recognition tasks show that the proposed
approach can alleviate the performance degradation experienced under unseen
data conditions by iteratively adapting the model to the unseen datas acoustic
condition.
