Unsupervised Speech Enhancement

Unsupervised speech enhancement aims to improve the quality of speech recordings without relying on paired clean and noisy data, a significant challenge in speech processing. Current research focuses on leveraging self-supervised learning techniques, generative models like diffusion models and variational autoencoders (VAEs), and incorporating elements like spectral kurtosis and attention mechanisms within deep neural networks to achieve robust noise reduction and dereverberation. These advancements offer the potential for more efficient and generalizable speech enhancement systems, impacting applications ranging from hearing aids and voice assistants to audio archiving and telecommunications.