  Training of speech enhancement systems often does not incorporate knowledge
of human perception and thus can lead to unnatural sounding results.
Incorporating psychoacoustically motivated speech perception metrics as part of
model training via a predictor network has recently gained interest. However,
the performance of such predictors is limited by the distribution of metric
scores that appear in the training data. In this work, we propose MetricGAN+/-
(an extension of MetricGAN+, one such metric-motivated system) which introduces
an additional network - a "de-generator" which attempts to improve the
robustness of the prediction network (and by extension of the generator) by
ensuring observation of a wider range of metric scores in training.
Experimental results on the VoiceBank-DEMAND dataset show relative improvement
in PESQ score of 3.8% (3.05 vs 3.22 PESQ score), as well as better
generalisation to unseen noise and speech.
