  This paper proposes a speech enhancement method which exploits the high
potential of residual connections in a Wide Residual Network architecture. This
is supported on single dimensional convolutions computed alongside the time
domain, which is a powerful approach to process contextually correlated
representations through the temporal domain, such as speech feature sequences.
We find the residual mechanism extremely useful for the enhancement task since
the signal always has a linear shortcut and the non-linear path enhances it in
several steps by adding or subtracting corrections. The enhancement capability
of the proposal is assessed by objective quality metrics evaluated with
simulated and real samples of reverberated speech signals. Results show that
the proposal outperforms the state-of-the-art method called WPE, which is known
to effectively reduce reverberation and greatly enhance the signal. The
proposed model, trained with artificial synthesized reverberation data, was
able to generalize to real room impulse responses for a variety of conditions
(e.g. different room sizes, $RT_{60}$, near & far field). Furthermore, it
achieves accuracy for real speech with reverberation from two different
datasets.
