  This paper models unsupervised learning of an identity-based pattern (or
copying) in speech called reduplication from raw continuous data with deep
convolutional neural networks. We use the ciwGAN architecture Begu\v{s} (2021a;
arXiv:2006.02951) in which learning of meaningful representations in speech
emerges from a requirement that the CNNs generate informative data. We propose
a technique to wug-test CNNs trained on speech and, based on four generative
tests, argue that the network learns to represent an identity-based pattern in
its latent space. By manipulating only two categorical variables in the latent
space, we can actively turn an unreduplicated form into a reduplicated form
with no other substantial changes to the output in the majority of cases. We
also argue that the network extends the identity-based pattern to unobserved
data. Exploration of how meaningful representations of identity-based patterns
emerge in CNNs and how the latent space variables outside of the training range
correlate with identity-based patterns in the output has general implications
for neural network interpretability.
