The lack of clean speech is a practical challenge to the development of
speech enhancement systems, which means that the training of neural network
models must be done in an unsupervised manner, and there is an inevitable
mismatch between their training criterion and evaluation metric. In response to
this unfavorable situation, we propose a teacher-student training strategy that
does not require any subjective/objective speech quality metrics as learning
reference by improving the previously proposed noisy-target training (NyTT).
Because homogeneity between in-domain noise and extraneous noise is the key to
the effectiveness of NyTT, we train various student models by remixing the
teacher model's estimated speech and noise for clean-target training or raw
noisy speech and the teacher model's estimated noise for noisy-target training.
We use the NyTT model as the initial teacher model. Experimental results show
that our proposed method outperforms several baselines, especially with
two-stage inference, where clean speech is derived successively through the
bootstrap model and the final student model.

本文提出了一种不需要主观 / 客观语音质量度量作为参考的教师 - 学生训练策略来改进先前提出的嘈杂目标训练策略，并使用该模型作为初始模型，实验结果表明我们的方法优于几个基准方法，尤其是在两阶段推理中。