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.

翻译：语音去混响旨在减轻后期混响成分带来的不利影响。尽管加权预测误差（WPE）方法在去混响中展现出优越性能，但在复杂噪声环境下，其性能与鲁棒性仍有提升空间。近期研究表明，融合基于物理模型与数据驱动的方法，可在保持可解释性的同时增强各类信号处理任务的性能。受此启发，本文提出一种新颖的去混响框架，在WPE框架中引入数据驱动方法以捕获语音先验信息。利用即插即用策略（PnP），特别是去噪正则化（RED）策略，在优化问题迭代求解过程中融入从数据中学习的语音先验信息。实验验证了所提方法的有效性。