Speech Enhancement Task

Speech enhancement aims to improve the quality of speech signals degraded by noise, reverberation, or other distortions, primarily focusing on improving intelligibility and perceptual quality. Recent research emphasizes the use of deep learning models, including generative adversarial networks (GANs), diffusion models, and transformer-based architectures, often incorporating techniques like multi-attention mechanisms, cross-domain feature fusion, and self-supervised learning to achieve state-of-the-art performance, particularly in challenging low SNR scenarios. These advancements have significant implications for various applications, such as hearing aids, voice assistants, and hands-free communication systems, by enabling more robust and effective speech processing in real-world environments. Furthermore, research is actively exploring model compression and acceleration techniques to make these advanced methods more computationally efficient for practical deployment.