标题： SwinSRGAN：基于Swin变换器的生成对抗网络用于高保真语音超分辨率 显示英文标题

标题： SwinSRGAN: Swin Transformer-based Generative Adversarial Network for High-Fidelity Speech Super-Resolution

摘要： 语音超分辨率（SR）从低分辨率语音信号中重建高频内容。 现有系统在两阶段梅尔声码器管道中常常存在表示不匹配，并且由仅使用CNN的生成器过度平滑幻觉的高频内容。 扩散和流模型计算成本高，其在不同领域和采样率下的鲁棒性仍然有限。 我们提出了SwinSRGAN，一个在修改离散余弦变换（MDCT）幅度上运行的端到端框架。 它是一个基于Swin Transformer的U-Net，通过结合时域MPD/MSD判别器与专门针对高频带的多频带MDCT判别器的混合对抗方案来捕捉长距离的谱时依赖关系。 我们对arcsinh压缩的MDCT应用了稀疏感知正则化器，以更好地保留瞬态成分。 该系统在一个步骤中将不同采样率的输入上采样到48 kHz，并实时运行。 在标准基准测试中，SwinSRGAN降低了客观误差并提高了ABX偏好分数。 在HiFi-TTS上无需微调的零样本测试中，它优于NVSR和mdctGAN，展示了在数据集上的强大泛化能力。 显示英文摘要

摘要： Speech super-resolution (SR) reconstructs high-frequency content from low-resolution speech signals. Existing systems often suffer from representation mismatch in two-stage mel-vocoder pipelines and from over-smoothing of hallucinated high-band content by CNN-only generators. Diffusion and flow models are computationally expensive, and their robustness across domains and sampling rates remains limited. We propose SwinSRGAN, an end-to-end framework operating on Modified Discrete Cosine Transform (MDCT) magnitudes. It is a Swin Transformer-based U-Net that captures long-range spectro-temporal dependencies with a hybrid adversarial scheme combines time-domain MPD/MSD discriminators with a multi-band MDCT discriminator specialized for the high-frequency band. We employs a sparse-aware regularizer on arcsinh-compressed MDCT to better preserve transient components. The system upsamples inputs at various sampling rates to 48 kHz in a single pass and operates in real time. On standard benchmarks, SwinSRGAN reduces objective error and improves ABX preference scores. In zero-shot tests on HiFi-TTS without fine-tuning, it outperforms NVSR and mdctGAN, demonstrating strong generalization across datasets