Clean Speech

Clean speech research focuses on enhancing audio signals by removing noise and distortions, improving speech quality and intelligibility for various applications. Current efforts concentrate on developing robust and efficient deep learning models, including diffusion models, transformers, and generative adversarial networks, often incorporating techniques like spectral analysis and knowledge distillation to achieve superior performance. This field is crucial for advancing speech recognition, emotion recognition, and other speech-based technologies, impacting areas like hearing aids, virtual assistants, and accessibility tools. The development of unsupervised and semi-supervised methods is a significant trend, addressing the limitations of data scarcity in training high-performing models.