Before revision:  We also argue that setting latent featural codes to values well beyond training range results in almost categorical generation of prototypical lexical items and reveals underlying values of each latent code . Probing deep neural networks trained on well understood dependencies in speech bears implications for latent space interpretability, understanding how deep neural networks learn meaningful representations, as well as a potential for unsupervised text-to-speech generation in the GAN framework .
After revision:  We also argue that setting latent featural codes to values well beyond training range results in almost categorical generation of prototypical lexical items and reveals underlying values of each latent code .