###### Abstract  
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.  
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Footnote †: journal: Medical Image Sciences  
0000-0001-8800-0000-0000-0000-0000-0000-0000-00000-0000-0000-0000-0000-00000-0000-00000-00000-00000-00000-00000-00000-00000-00000-0000  
Our first study on voice analysis using sEMG [12] employed a newly developed hierarchical approach based on the Guided Under-Determined Source Signal Separation (GUSSS) ratios [13] to classify what was referred to as _vocal gestures_. The study used four sEMG channels to collect signals from the anterior neck, but it was performed on a single test subject. As a result, the system achieved 85% accuracy in classifying six voice gestures. In our following study [14], the same classification system was applied to a larger set of voice gestures including simulated pressed vowel productions and normal productions (/a/,/u/, and /i/). The approach was tested on ten vocally healthy subjects and achieved an overall accuracy of 85% for classifying ten distinct gestures and 95% for detecting normal and simulated pressed vowel productions. More recently, in [15], we continued this line of research and collected data on 37 test subjects divided into two groups labeled: vocally healthy and vocally fatigued, according to self-reported VFI-1 scores--VFI-1 is the first factor of the Vocal Fatigue Index: thefness of voice and avoidance of voice use [5]. Our initial approach using GUSSS was improved by using five additional sEMG features. The results in the classification of healthy and fatigued voice productions reached 96% accuracy under intra-subject testing conditions. For the same classification, but under inter-subject conditions, the overall accuracy was 94%. Despite the good results in accuracy, this latest study pointed to potential concerns regarding data imbalance, as indicated by the low sensitivity achieved of 0.58.  
Therefore, in this paper, we performed an extensive investigation of the challenges, as well as improvements in our approach to the classification of vocal fatigue using a substantially larger dataset, expanding the data set in [15] from 37 to 88. The overarching aim was to establish a reference dataset of extralaryngeal activity to determine the validity and reliability of pattern recognition of sEMG signals for detecting vocal fatigue. In that sense, we first explored the benefits of sEMG signal normalization by utilizing skinfold thickness and maximum voluntary contraction (MVC) trials. We then addressed the problem of data imbalance to improve the classification of vocal fatigue. Finally, we examined our classification experiments with an increased total sample size to address how to achieve better generalization in order to be clinically adoptable.