MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement
arXiv:2104.03538
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).
arXiv commentAccepted by Interspeech 2021
References in corpus (7)
- Spectral Normalization for Generative Adversarial Networks
- An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks
- MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement
- Learning with Learned Loss Function: Speech Enhancement with Quality-Net to Improve Perceptual Evaluation of Speech Quality
- DNN-based Source Enhancement to Increase Objective Sound Quality Assessment Score
- End-to-End Multi-Task Denoising for joint SDR and PESQ Optimization
- Acoustics-guided evaluation (AGE): a new measure for estimating performance of speech enhancement algorithms for robust ASR
Cited by in corpus (5)
- SpeechBrain: A General-Purpose Speech Toolkit
- Glance and Gaze: A Collaborative Learning Framework for Single-channel Speech Enhancement
- Unsupervised Noise Adaptive Speech Enhancement by Discriminator-Constrained Optimal Transport
- TENET: A Time-reversal Enhancement Network for Noise-robust ASR
- Deep Learning-based Non-Intrusive Multi-Objective Speech Assessment Model with Cross-Domain Features