  Within the area of speech enhancement, there is an ongoing interest in the
creation of neural systems which explicitly aim to improve the perceptual
quality of the processed audio. In concert with this is the topic of
non-intrusive (i.e. without clean reference) speech quality prediction, for
which neural networks are trained to predict human-assigned quality labels
directly from distorted audio. When combined, these areas allow for the
creation of powerful new speech enhancement systems which can leverage large
real-world datasets of distorted audio, by taking inference of a pre-trained
speech quality predictor as the sole loss function of the speech enhancement
system. This paper aims to identify a potential pitfall with this approach,
namely hallucinations which are introduced by the enhancement system `tricking'
the speech quality predictor.
