Abstract
Applying speech super-resolution (SR) to recordings with severely low sampling rates is a critical challenge in digital archiving and investigative audio recovery. In these scenarios, the input lacks essential acoustic cues. Consequently, existing generative models often fail; without sufficient context, they hallucinate phonetic content, guessing words based on probability rather than meaning. To address this, we propose CogSR, a framework designed specifically for high-precision, offline restoration. Our approach shifts the focus from simple signal mapping to cognitive reconstruction. By integrating a Large Audio-Language Model, we employ Chain-of-Thought reasoning to act as a semantic anchor, while explicit acoustic priors ensure the speaker's identity remains consistent. This guides a Rectified Flow backbone to synthesize high-frequency details that are not only realistic but linguistically accurate. Evaluations show that CogSR effectively eliminates ambiguity in severe degradation regimes, making it a robust solution for restoring high-value legacy and surveillance audio.
Abstract (translated)
将语音超分辨率（SR）技术应用于采样率极低的录音，在数字档案和调查音频恢复领域是一个关键挑战。在这种情况下，输入缺乏重要的声学线索。因此，现有的生成模型往往失败；由于缺少足够的上下文信息，它们会臆造出可能不符合实际意义的声音内容。为了解决这一问题，我们提出了一种名为CogSR的框架，该框架专门用于高精度、离线恢复任务。我们的方法将重点从简单的信号映射转移到认知重建上。通过整合大型音频-语言模型，我们利用链式思维推理充当语义锚点，同时显式的声学先验确保说话人的身份一致性。这引导了一个校正流主干网络合成高频细节，这些细节不仅现实而且在语音学上准确无误。 评估结果显示，CogSR能够有效消除严重退化情况下的模糊性，使其成为恢复高价值的历史和监控音频的可靠解决方案。
URL
https://arxiv.org/abs/2512.16304
