  Perceptual evaluation of speech quality (PESQ) requires a clean speech
reference as input, but predicts the results from (reference-free) absolute
category rating (ACR) tests. In this work, we train a fully convolutional
recurrent neural network (FCRN) as deep noise suppression (DNS) model, with
either a non-intrusive or an intrusive PESQNet, where only the latter has
access to a clean speech reference. The PESQNet is used as a mediator providing
a perceptual loss during the DNS training to maximize the PESQ score of the
enhanced speech signal. For the intrusive PESQNet, we investigate two
topologies, called early-fusion (EF) and middle-fusion (MF) PESQNet, and
compare to the non-intrusive PESQNet to evaluate and to quantify the benefits
of employing a clean speech reference input during DNS training. Detailed
analyses show that the DNS trained with the MF-intrusive PESQNet outperforms
the Interspeech 2021 DNS Challenge baseline and the same DNS trained with an
MSE loss by 0.23 and 0.12 PESQ points, respectively. Furthermore, we can show
that only marginal benefits are obtained compared to the DNS trained with the
non-intrusive PESQNet. Therefore, as ACR listening tests, the PESQNet does not
necessarily require a clean speech reference input, opening the possibility of
using real data for DNS training.
