Noisy Reverberant

Noisy reverberant speech processing aims to improve the quality and intelligibility of audio signals degraded by both background noise and reverberation, a common challenge in real-world speech applications. Current research focuses on developing robust deep learning models, including transformer-based architectures and generative adversarial networks (GANs), often incorporating multi-channel processing and techniques like beamforming and Kalman filtering to enhance speech separation and dereverberation. These advancements are crucial for improving the performance of automatic speech recognition, speech translation, and other speech-based technologies in diverse and challenging acoustic environments. The ultimate goal is to create more robust and reliable systems for various applications, from hearing aids to virtual assistants.