Frame-level speech enhancement based on Wasserstein GAN

Abstract

Speech enhancement is a challenging and critical task in the speech processing research area. In this paper, we propose a novel speech enhancement model based on Wasserstein generative adversarial networks, called WSEM. The proposed model operates on frame-level speech segments by using an adjacent frames extension mechanism, to enforce the mapping from noisy speech to the clean target, which makes it distinctly different from other related GAN-based models. We compare the performance of WSEM with related works on benchmark datasets under different signal-to-noise (SNR) conditions, experimental results show that WSEM performs comparable to the state-of-the-art approaches in all the tests, and it performs especially well in low SNR environments.