  Speech dereverberation aims to alleviate the detrimental effects of
late-reverberant components. While the weighted prediction error (WPE) method
has shown superior performance in dereverberation, there is still room for
further improvement in terms of performance and robustness in complex and noisy
environments. Recent research has highlighted the effectiveness of integrating
physics-based and data-driven methods, enhancing the performance of various
signal processing tasks while maintaining interpretability. Motivated by these
advancements, this paper presents a novel dereverberation frame-work, which
incorporates data-driven methods for capturing speech priors within the WPE
framework. The plug-and-play strategy (PnP), specifically the regularization by
denoising (RED) strategy, is utilized to incorporate speech prior information
learnt from data during the optimization problem solving iterations.
Experimental results validate the effectiveness of the proposed approach.
