As more speech technologies rely on a supervised deep learning approach with
clean speech as the ground truth, a methodology to onboard said speech at scale
is needed. However, this approach needs to minimize the dependency on human
listening and annotation, only requiring a human-in-the-loop when needed. In
this paper, we address this issue by outlining Speech Enhancement-based
Curation Pipeline (SECP) which serves as a framework to onboard clean speech.
This clean speech can then train a speech enhancement model, which can further
refine the original dataset and thus close the iterative loop. By running two
iterative rounds, we observe that enhanced output used as ground truth does not
degrade model performance according to $\Delta_{PESQ}$, a metric used in this
paper. We also show through comparative mean opinion score (CMOS) based
subjective tests that the highest and lowest bound of refined data is
perceptually better than the original data.

简化后：本文提出了一种基于语音增强的策划管道（SECP）框架，旨在最小化对人工听力和注释的依赖，通过两个迭代循环的运行，观察到使用增强输出作为基本真实性的模型性能不降低，并通过主观测试表明改进数据的最高和最低边界比原始数据更具感知效果。