  Pre-emphasis filtering, compensating for the natural energy decay of speech
at higher frequencies, has been considered as a common pre-processing step in a
number of speech processing tasks over the years. In this work, we demonstrate,
for the first time, that pre-emphasis filtering may also be used as a simple
and computationally-inexpensive way to leverage deep neural network-based
speech enhancement performance. Particularly, we look into pre-emphasizing the
estimated and actual clean speech prior to loss calculation so that different
speech frequency components better mirror their perceptual importance during
the training phase. Experimental results on a noisy version of the TIMIT
dataset show that integrating the pre-emphasis-based methodology at hand yields
relative estimated speech quality improvements of up to 4.6% and 3.4% for noise
types seen and unseen, respectively, during the training phase. Similar to the
case of pre-emphasis being considered as a default pre-processing step in
classical automatic speech recognition and speech coding systems, the
pre-emphasis-based methodology analyzed in this article may potentially become
a default add-on for modern speech enhancement.
