  In recent years, Generative Adversarial Networks (GANs) have produced
significantly improved results in speech enhancement (SE) tasks. They are
difficult to train, however. In this work, we introduce several improvements to
the GAN training schemes, which can be applied to most GAN-based SE models. We
propose using consistency loss functions, which target the inconsistency in
time and time-frequency domains caused by Fourier and Inverse Fourier
Transforms. We also present self-correcting optimization for training a GAN
discriminator on SE tasks, which helps avoid "harmful" training directions for
parts of the discriminator loss function. We have tested our proposed methods
on several state-of-the-art GAN-based SE models and obtained consistent
improvements, including new state-of-the-art results for the Voice Bank+DEMAND
dataset.
