SECP：一种基于语音增强的、用于可扩展获取干净语音的策展流水线
SECP: A Speech Enhancement-Based Curation Pipeline For Scalable Acquisition Of Clean Speech
AI总结：
本文提出基于语音增强的策展流水线SECP，通过迭代获取和精炼干净语音，实验表明增强输出作为标签不会降低模型性能，且精炼数据在主观听感上优于原始数据。
摘要：
随着越来越多的语音技术依赖以干净语音为真实标签的监督式深度学习方法，需要一种能够大规模引入此类语音的方法。然而，这种方法需要尽量减少对人类听音和标注的依赖，仅在必要时才引入人在回路。在本文中，我们通过提出基于语音增强的策展流水线（SECP）来解决这一问题，该流水线作为一个引入干净语音的框架。这些干净语音随后可用于训练语音增强模型，该模型能够进一步精炼原始数据集，从而闭合迭代循环。通过运行两轮迭代，我们观察到，根据本文所使用的指标 $Δ_{PESQ}$，将增强输出用作真实标签不会降低模型性能。我们还通过基于比较平均意见分（CMOS）的主观测试表明，精炼后数据的最高和最低边界在感知上均优于原始数据。
英文摘要：
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 $Δ_{PESQ}$, 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
