Our previous studies demonstrated that it is possible to perform the classification of both simulated pressed and actual vocally fatigued voice productions versus vocally healthy productions through the pattern recognition of sEMG signals obtained from subjects’ anterior neck. In these studies, the commonly accepted Vocal Fatigue Index factor 1 (VFI-1) was used for the ground-truth labeling of normal versus vocally fatigued voice productions. Through recent experiments, other factors with potential effects on classification were also studied, such as sEMG signal normalization, and data imbalance—i.e., the large difference between the number of vocally healthy subjects and of those with vocal fatigue. Therefore, in this paper, we present a much improved classification method derived from an extensive study of the effects of such extrinsic factors on the classification of vocal fatigue. The study was performed on a large number of sEMG signals from 88 vocally healthy and fatigued subjects including student teachers and teachers and it led to important conclusions on how to optimize a machine learning approach for the early detection of vocal fatigue.

中文翻译：

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使用sEMG对人声疲劳进行分类：数据不平衡，归一化以及人声疲劳指数评分的作用

我们以前的研究表明，通过对从受试者前颈获得的sEMG信号进行模式识别，可以对模拟的按声的和实际的发声疲劳的声音产生与发声的健康产生进行分类。在这些研究中，公认的人声疲劳指数因子1（VFI-1）用于正常和发声疲劳的声音产生的地面真相标记。通过最近的实验，还研究了其他可能对分类产生影响的因素，例如sEMG信号归一化和数据不平衡，即，声音健康受试者的数量与声音疲劳受试者的数量之间存在较大差异。因此，在本文中，我们提出了一种改进的分类方法，该方法源自对此类外在因素对声疲劳分类的影响的广泛研究。这项研究是对来自88名声音健康和疲倦受试者（包括学生教师和教师）的大量sEMG信号进行的，得出了关于如何优化机器学习方法以早期发现声音疲劳的重要结论。