Latent Discriminative Conditioning Effects on GAN-Based Speech Enhancement Robustness in Extreme Noise

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 goal: What is the impact of latent discriminative conditioning on the robustness of GAN-based speech enhancement models compared to diffusion priors in extreme noise scenarios?

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