Reexamining the Efficacy of MetricGAN for Speech Enhancement

Abstract

MetricGAN, a notable generative approach, provides an effective framework to train speech enhancement models to produce high metric scores. However, we identify two key limitations of current MetricGAN-family models, i.e. neglecting certain mainstream metrics during evaluation and conducting evaluation exclusively at high SNR. Firstly, we comprehensively assess MetricGAN models using mainstream metrics, surprisingly revealing MetricGAN models produce worse SISDR and STOI than unprocessed noisy speech. Secondly, we demonstrate that training MetricGAN models at low SNR often results in convergence to biased local minima, where PESQ scores are inflated while their SISDR and STOI values deteriorate significantly. In addition, we propose and validate two training tricks to address these issues: SISDR regularization and mixture-of-actor training. We find that these tricks effectively guide MetricGAN models to avoid local minima, thus improving speech quality.