MOS-GAN: Mean Opinion Score GAN for Unsupervised Speech Enhancement

Abstract

Deep learning-based speech enhancement methods are predominantly trained in a supervised manner, relying on synthesized paired noisy-to-clean data. However, acquiring clean speech in real-world scenarios is often difficult or even impractical. To overcome this limitation, we propose a novel unsupervised learning framework for speech enhancement that relies solely on observed noisy speech, called MOS-GAN. Specifically, we leverage generative adversarial networks (GANs), where the generator (the enhancement model) is optimized to maximize the mean opinion score (MOS) guided by a discriminator, while the discriminator (a non-intrusive speech quality metric model) is optimized to predict MOS. However, without using reference clean speech, directly training of MOS-GAN is unstable and cannot achieve satisfactory performance. To address this issue, we further incorporate an unsupervised prior loss to substantially enhance training performance. Experimental results on benchmarks demonstrate that the proposed method, which requires neither clean data nor teacher models, performs on par with leading self-supervised and unsupervised approaches.