Speech Super Resolution

Speech super-resolution (SSR) aims to enhance low-resolution speech recordings by reconstructing missing high-frequency information, improving audio quality and intelligibility. Recent research emphasizes developing efficient and robust models, often employing deep neural networks such as transformers and diffusion models, sometimes combined with techniques like modified discrete cosine transforms (MDCT) for improved phase reconstruction. These advancements focus on achieving high fidelity at low computational cost, even on resource-constrained devices, and address challenges like noise reduction and generalization to real-world, variable conditions. The resulting improvements in speech quality have significant implications for applications ranging from mobile communication to assistive technologies.