  It has been shown recently that deep learning based models are effective on
speech quality prediction and could outperform traditional metrics in various
perspectives. Although network models have potential to be a surrogate for
complex human hearing perception, they may contain instabilities in
predictions. This work shows that deep speech quality predictors can be
vulnerable to adversarial perturbations, where the prediction can be changed
drastically by unnoticeable perturbations as small as $-30$ dB compared with
speech inputs. In addition to exposing the vulnerability of deep speech quality
predictors, we further explore and confirm the viability of adversarial
training for strengthening robustness of models.
