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

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 goal: 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?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.7/10.