Discriminative Latent Feature Integration in GAN-Based Speech Enhancement: Convergence and Stability Analysis

Abstract

Abstract: Enhancing speech quality under adverse SNR conditions remains a significant challenge for discriminative deep neural network (DNN)-based approaches. In this work, we propose DisCoGAN, which is a time-frequency-domain generative adversarial network (GAN) conditioned by the latent features of a discriminative model pre-trained for speech enhancement in low SNR scenarios. Our proposed method achieves superior performance compared to state-of-the-arts discriminative methods and also surpasses end-to-end (E2E) trained GAN models. We also investigate the impact of various configurations for conditio

Research Question

What is the impact of integrating discriminative latent features on the convergence speed and training stability of GAN-based speech enhancement models compared to standard diffusion baselines?

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