Universal Speech Enhancement

Universal speech enhancement aims to create single models capable of cleaning speech degraded by a wide variety of distortions, including noise, reverberation, and artifacts, surpassing the limitations of systems designed for specific noise types. Current research focuses on generative models, particularly score-based diffusion methods, often incorporating adversarial training and techniques to improve content preservation and handle diverse input conditions like varying audio lengths and microphone characteristics. This pursuit of a universal solution holds significant promise for improving the robustness and applicability of speech processing technologies across various applications, from virtual assistants to hearing aids.