In unsupervised or weakly-supervised approaches for speech dereverberation, the target clean (dry) signals are considered to be unknown during training. In that context, evaluating to what extent information can be retrieved from the sole knowledge of reverberant (wet) speech becomes critical. This work investigates the role of the reverberant (wet) phase in the time-frequency domain. Based on Statistical Wave Field Theory, we show that late reverberation perturbs phase components with white, uniformly distributed noise, except at low frequencies. Consequently, the wet phase carries limited useful information and is not essential for weakly supervised dereverberation. To validate this finding, we train dereverberation models under a recent weak supervision framework and demonstrate that performance can be significantly improved by excluding the reverberant phase from the loss function.

翻译：在无监督或弱监督的语音去混响方法中，训练过程中目标干净（干）信号被视为未知。在此背景下，评估仅凭混响（湿）语音的知识能恢复多少信息变得至关重要。本研究探讨了时频域中混响（湿）相位的作用。基于统计波场理论，我们证明除低频外，晚期混响会以均匀分布的白噪声形式干扰相位分量。因此，湿相位携带的有用信息有限，对弱监督去混响并非必需。为验证这一发现，我们在近期弱监督框架下训练去混响模型，并证明通过从损失函数中排除混响相位可显著提升性能。