用判别模型的隐向量增强生成式语音降噪，尤其在极低信噪比下表现更好。
Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement
- 从判别模型提取隐特征作为条件输入，指导生成模型优化
- 在低于-10dB信噪比下显著提升语音质量，优于现有生成方法
- 适合需要高鲁棒性语音增强的场景，如通信、听障辅助
基于生成对抗网络（GAN）和扩散模型的生成式语音增强方法在多种任务中表现良好，但在极低信噪比（SNR）条件下仍缺乏充分研究且性能受限。为此，本文提出一种新方法——DisCoGAN，利用判别式语音增强模型提取的潜在特征作为通用条件信号，提升基于GAN的语音增强效果。实验表明，该方法在低SNR场景下明显优于基线模型，同时在高SNR及真实录音数据上保持竞争力或更优表现。我们还系统评估了端到端训练、预处理阶段与后滤波三种经典GAN架构，以及判别模型在低SNR下的表现。结果证明DisCoGAN始终领先。最后通过消融实验分析各组件贡献，并验证判别条件机制对整体性能的积极影响。
原文摘要 · Abstract (English)
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.
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