2025/08/28 by Shrishti Saha Shetu, Shetu, Shrishti Saha, Emanuël A. P. Habets +3 · 1 citation
Computer Science · #Audio and Speech Processing (eess.AS) #FOS: Electrical engineering #Speech Recognition and Synthesis #Speech and Audio Processing #Speech and dialogue systems #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2508.20859
openalex publication_date 2025/08/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 proposed method, referred to as DisCoGAN, demonstrates performance improvements over baseline models, particularly in low-SNR scenarios, while also maintaining competitive or superior performance in high-SNR conditions and on real-world recordings. We also conduct a comprehensive evaluation of conventional GAN-based architectures, including GANs trained end-to-end, GANs as a first processing stage, and post-filtering GANs, as well as discriminative models under low-SNR conditions. We show that DisCoGAN consistently outperforms existing methods. Finally, we present an ablation study that investigates the contributions of individual components within DisCoGAN and analyzes the impact of the discriminative conditioning method on overall performance.