  Generative Adversarial Networks (GANs) are machine learning networks based
around creating synthetic data. Voice Conversion (VC) is a subset of voice
translation that involves translating the paralinguistic features of a source
speaker to a target speaker while preserving the linguistic information. The
aim of non-parallel conditional GANs for VC is to translate an acoustic speech
feature sequence from one domain to another without the use of paired data. In
the study reported here, we investigated the interpretability of
state-of-the-art implementations of non-parallel GANs in the domain of VC. We
show that the learned representations in the repeating layers of a particular
GAN architecture remain close to their original random initialised parameters,
demonstrating that it is the number of repeating layers that is more
responsible for the quality of the output. We also analysed the learned
representations of a model trained on one particular dataset when used during
transfer learning on another dataset. This showed extremely high levels of
similarity across the entire network. Together, these results provide new
insight into how the learned representations of deep generative networks change
during learning and the importance in the number of layers.
