  This paper presents a technique to interpret and visualize intermediate
layers in generative CNNs trained on raw speech data in an unsupervised manner.
We argue that averaging over feature maps after ReLU activation in each
transpose convolutional layer yields interpretable time-series data. This
technique allows for acoustic analysis of intermediate layers that parallels
the acoustic analysis of human speech data: we can extract F0, intensity,
duration, formants, and other acoustic properties from intermediate layers in
order to test where and how CNNs encode various types of information. We
further combine this technique with linear interpolation of a model's latent
space to show a causal relationship between individual variables in the latent
space and activations in a model's intermediate convolutional layers. In
particular, observing the causal effect between linear interpolation and the
resulting changes in intermediate layers can reveal how individual latent
variables get transformed into spikes in activation in intermediate layers. We
train and probe internal representations of two models -- a bare WaveGAN
architecture and a ciwGAN extension which forces the Generator to output
informative data and results in the emergence of linguistically meaningful
representations. Interpretation and visualization is performed for three basic
acoustic properties of speech: periodic vibration (corresponding to vowels),
aperiodic noise vibration (corresponding to fricatives), and silence
(corresponding to stops). The proposal also allows testing of higher-level
morphophonological alternations such as reduplication (copying). In short,
using the proposed technique, we can analyze how linguistically meaningful
units in speech get encoded in each convolutional layer of a generative neural
network.
