Enhanced Speech

Enhanced speech research aims to improve the clarity and intelligibility of speech degraded by noise, reverberation, or other distortions, primarily through the development of advanced signal processing and machine learning techniques. Current research heavily utilizes deep neural networks, including variations of autoencoders, generative adversarial networks (GANs), and diffusion models, often incorporating multimodal data (audio-visual) and novel loss functions focused on perceptual quality and intelligibility metrics (e.g., PESQ, STOI). These advancements hold significant promise for improving the performance of speech recognition systems, assistive listening devices, and human-computer interaction in challenging acoustic environments.