  In this paper, in order to further deal with the performance degradation
caused by ignoring the phase information in conventional speech enhancement
systems, we proposed a temporal dilated convolutional generative adversarial
network (TDCGAN) in the end-to-end based speech enhancement architecture. For
the first time, we introduced the temporal dilated convolutional network with
depthwise separable convolutions into the GAN structure so that the receptive
field can be greatly increased without increasing the number of parameters. We
also first explored the effect of signal-to-noise ratio (SNR) penalty item as
regularization of the loss function of generator on improving the SNR of
enhanced speech. The experimental results demonstrated that our proposed method
outperformed the state-of-the-art end-to-end GAN-based speech enhancement.
Moreover, compared with previous GAN-based methods, the proposed TDCGAN could
greatly decreased the number of parameters. As expected, the work also
demonstrated that the SNR penalty item as regularization was more effective
than $L1$ on improving the SNR of enhanced speech.
