Classification of Vocal Fatigue Using sEMG: Data Imbalance, Normalization, and the Role of Vocal Fatigue Index Scores

Applied Sciences

Maria Dietrich¹  Yixiang Gao²  GuilhermeN. DeSouza² 

[1] Department of Psychiatry and Psychotherapy, University Hospital Bonn, 53127 Bonn, Germany;ViGIR Lab, Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65201, USA;

关键词: surface electromyography;    pattern recognition;    biomedical monitoring;    support vector machine;    vocal fatigue;    voice disorders;   

DOI  :  10.3390/app11104335

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown