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

MetricGAN+/-: Increasing Robustness of Noise Reduction on Unseen Data