MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement
Abstract
The discrepancy between the cost function used for training a speech enhancement model and human auditory perception usually makes the quality of enhanced speech unsatisfactory. Objective evaluation metrics which consider human perception can hence serve as a bridge to reduce the gap. Our previously proposed MetricGAN was designed to optimize objective metrics by connecting the metric with a discriminator. Because only the scores of the target evaluation functions are needed during training, the metrics can even be non-differentiable. In this study, we propose a MetricGAN+ in which three training techniques incorporating domain-knowledge of speech processing are proposed. With these techniques, experimental results on the VoiceBank-DEMAND dataset show that MetricGAN+ can increase PESQ score by 0.3 compared to the previous MetricGAN and achieve state-of-the-art results (PESQ score = 3.15).
Keywords
Cite
@article{arxiv.2104.03538,
title = {MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement},
author = {Szu-Wei Fu and Cheng Yu and Tsun-An Hsieh and Peter Plantinga and Mirco Ravanelli and Xugang Lu and Yu Tsao},
journal= {arXiv preprint arXiv:2104.03538},
year = {2021}
}
Comments
Accepted by Interspeech 2021