  Recent advances in neural-network based generative modeling of speech has
shown great potential for speech coding. However, the performance of such
models drops when the input is not clean speech, e.g., in the presence of
background noise, preventing its use in practical applications. In this paper
we examine the reason and discuss methods to overcome this issue. Placing a
denoising preprocessing stage when extracting features and target clean speech
during training is shown to be the best performing strategy.
