GAN-Based Speech Enhancement with Discriminative Latents versus Diffusion Priors under Extreme Noise and Adversarial Attacks

Abstract

Abstract: Generative speech enhancement methods based on generative adversarial networks (GANs) and diffusion models have shown promising results in various speech enhancement tasks. However, their performance in very low signal-to-noise ratio (SNR) scenarios remains under-explored and limited, as these conditions pose significant challenges to both discriminative and generative state-of-the-art methods. To address this, we propose a method that leverages latent features extracted from discriminative speech enhancement models as generic conditioning features to improve GAN-based speech enhancement. The

Research Question

How does the conditioning of GAN-based speech enhancement models with discriminative latents affect their robustness against adversarial attacks (measured by adversarial success rate) compared to diffusion priors in extreme noise scenarios?

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