This paper introduces a new training strategy to improve speech dereverberation systems in an unsupervised manner using only reverberant speech. Most existing algorithms rely on paired dry/reverberant data, which is difficult to obtain. Our approach uses limited acoustic information, like the reverberation time (RT60), to train a dereverberation system. Experimental results demonstrate that our method achieves more consistent performance across various objective metrics than the state-of-the-art.

翻译：本文提出了一种新的训练策略，仅利用混响语音以非监督方式改进语音去混响系统。现有算法大多依赖成对的干声/混响数据，此类数据难以获取。我们的方法利用有限的声学信息（如混响时间RT60）来训练去混响系统。实验结果表明，相较于现有最优方法，本方法在多种客观指标上取得了更稳定的性能。