Comparative Analysis of Diffusion and GAN Architectures for Speaker Embedding Consistency in Low-SNR Speech Enhancement

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 do diffusion-based speech enhancement models compare to GAN-based architectures in preserving speaker embedding consistency on the VoxCeleb1-H test set under SNR conditions below 0dB?

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