  The reliability of using fully convolutional networks (FCNs) has been
successfully demonstrated by recent studies in many speech applications. One of
the most popular variants of these FCNs is the `U-Net', which is an
encoder-decoder network with skip connections. In this study, we propose
`SkipConvNet' where we replace each skip connection with multiple convolutional
modules to provide decoder with intuitive feature maps rather than encoder's
output to improve the learning capacity of the network. We also propose the use
of optimal smoothing of power spectral density (PSD) as a pre-processing step,
which helps to further enhance the efficiency of the network. To evaluate our
proposed system, we use the REVERB challenge corpus to assess the performance
of various enhancement approaches under the same conditions. We focus solely on
monitoring improvements in speech quality and their contribution to improving
the efficiency of back-end speech systems, such as speech recognition and
speaker verification, trained on only clean speech. Experimental findings show
that the proposed system consistently outperforms other approaches.
