  Most deep noise suppression (DNS) models are trained with reference-based
losses requiring access to clean speech. However, sometimes an additive
microphone model is insufficient for real-world applications. Accordingly, ways
to use real training data in supervised learning for DNS models promise to
reduce a potential training/inference mismatch. Employing real data for DNS
training requires either generative approaches or a reference-free loss without
access to the corresponding clean speech. In this work, we propose to employ an
end-to-end non-intrusive deep neural network (DNN), named PESQ-DNN, to estimate
perceptual evaluation of speech quality (PESQ) scores of enhanced real data. It
provides a reference-free perceptual loss for employing real data during DNS
training, maximizing the PESQ scores. Furthermore, we use an epoch-wise
alternating training protocol, updating the DNS model on real data, followed by
PESQ-DNN updating on synthetic data. The DNS model trained with the PESQ-DNN
employing real data outperforms all reference methods employing only synthetic
training data. On synthetic test data, our proposed method excels the
Interspeech 2021 DNS Challenge baseline by a significant 0.32 PESQ points. Both
on synthetic and real test data, the proposed method beats the baseline by 0.05
DNSMOS points - although PESQ-DNN optimizes for a different perceptual metric.
