  Popular neural network-based speech enhancement systems operate on the
magnitude spectrogram and ignore the phase mismatch between the noisy and clean
speech signals. Conditional generative adversarial networks (cGANs) show
promise in addressing the phase mismatch problem by directly mapping the raw
noisy speech waveform to the underlying clean speech signal. However,
stabilizing and training cGAN systems is difficult and they still fall short of
the performance achieved by the spectral enhancement approaches. This paper
investigates whether different normalization strategies and one-sided label
smoothing can further stabilize the cGAN-based speech enhancement model. In
addition, we propose incorporating a Gammatone-based auditory filtering layer
and a trainable pre-emphasis layer to further improve the performance of the
cGAN framework. Simulation results show that the proposed approaches improve
the speech enhancement performance of cGAN systems in addition to yielding
improved stability and reduced computational effort.
