Speech Reconstruction

Speech reconstruction focuses on recovering high-quality speech signals from various degraded or incomplete sources, aiming to improve speech intelligibility and quality. Current research emphasizes developing efficient and robust deep learning models, including generative adversarial networks (GANs), transformers, and autoencoders, often incorporating techniques like vector quantization and transfer learning to enhance performance with limited data. These advancements have significant implications for applications such as hearing aid technology, brain-computer interfaces, and speech enhancement in noisy environments, improving communication accessibility and quality.