标题： SaD：一种场景感知的语音增强鉴别器 显示英文标题

标题： SaD: A Scenario-Aware Discriminator for Speech Enhancement

摘要： 基于生成对抗网络的模型在语音增强领域表现出色。 然而，当前这些模型的优化策略主要集中在改进生成器的架构或提升判别器的质量评估指标上。 这种方法常常忽略了不同场景中固有的丰富上下文信息。 在本文中，我们提出了一种场景感知的判别器，该判别器能够捕捉场景特定特征并执行频域划分，从而实现对生成器生成的增强语音更准确的质量评估。 我们在三个代表性模型上使用两个公开数据集进行了全面实验。 结果表明，我们的方法可以在不改变生成器结构的情况下有效适应各种生成器架构，从而在不同场景下的语音增强中实现进一步的性能提升。 显示英文摘要

摘要： Generative adversarial network-based models have shown remarkable performance in the field of speech enhancement. However, the current optimization strategies for these models predominantly focus on refining the architecture of the generator or enhancing the quality evaluation metrics of the discriminator. This approach often overlooks the rich contextual information inherent in diverse scenarios. In this paper, we propose a scenario-aware discriminator that captures scene-specific features and performs frequency-domain division, thereby enabling a more accurate quality assessment of the enhanced speech generated by the generator. We conducted comprehensive experiments on three representative models using two publicly available datasets. The results demonstrate that our method can effectively adapt to various generator architectures without altering their structure, thereby unlocking further performance gains in speech enhancement across different scenarios.