  Understanding how deep convolutional neural networks classify data has been
subject to extensive research. This paper proposes a technique to visualize and
interpret intermediate layers of unsupervised deep convolutional networks by
averaging over individual feature maps in each convolutional layer and
inferring underlying distributions of words with non-linear regression
techniques. A GAN-based architecture (ciwGAN arXiv:2006.02951) that includes a
Generator, a Discriminator, and a classifier was trained on unlabeled sliced
lexical items from TIMIT. The training process results in a deep convolutional
network that learns to classify words into discrete classes only from the
requirement of the Generator to output informative data. This classifier
network has no access to the training data -- only to the generated data. We
propose a technique to visualize individual convolutional layers in the
classifier that yields highly informative time-series data for each
convolutional layer and apply it to unobserved test data. Using non-linear
regression, we infer underlying distributions for each word which allows us to
analyze both absolute values and shapes of individual words at different
convolutional layers, as well as perform hypothesis testing on their acoustic
properties. The technique also allows us to test individual phone contrasts and
how they are represented at each layer.
