## 4 Experiments
### Classification of Matched-Subject Groups
#### 4.2.1 Normalization Using 100% MVC  
In addition to matching our subjects by age and skinfold thickness, we also investigated the possibility of using MVC as a method for amplitude normalization [24]. In this case, the approach was to normalize all vowel productions by the subject's 100% MVC measurements during data collection. First, a moving window was employed to average the absolute value of the sEMG signals from each of the four channels during 100% MVC. Then, the maximum moving-window average from each channel was determined and used to normalize the sEMG signals collected during voice productions. Table 6 shows the results after applying the 100% MVC amplitude normalization to the sEMG signals and then classifying the signals using each feature separately. The goal was to determine the impact of 100% MVC normalization on each feature, as well as on groups of features: i.e., amplitude-dependent features, such as MAV, RMS, and WL; frequency-dependent features, such as ZC, SSC, and WA; and stochastic features, such as AR and GR. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Before MVC Normalization**} & \multicolumn{4}{c}{**After MVC Normalization**} \\ \hline  & **Valid** & **Acc** & **Sens** & **Spec** & **Valid** & **Acc** & **Sens** & **Spec** \\ \hline  & \multicolumn{4}{c}{Frequency-dependent features a  } \\ \hline ZC & 83.07\% & 70.52\% & 0.74 & 0.67 & 83.09\% & 70.65\% & 0.74 & 0.67 \\ SSC & 84.01\% & 69.49\% & 0.76 & 0.63 & 84.00\% & 69.47\% & 0.76 & 0.63 \\ WA & 85.19\% & 73.62\% & 0.77 & 0.70 & 85.17\% & 73.55\% & 0.77 & 0.70 \\ Avg & 96.74\% & 63.78\% & 0.62 & 0.65 & 96.74\% & 63.78\% & 0.62 & 0.65 \\ \hline  & \multicolumn{4}{c}{Amplitude-dependent features a  } \\ \hline MAV & 71.05\% & 38.00\% & 0.34 & 0.42 & 76.90\% & 46.13\% & 0.43 & 0.49 \\ RMS & 70.47\% & 39.09\% & 0.35 & 0.43 & 76.77\% & 43.90\% & 0.40 & 0.47 \\ WL & 70.66\% & 41.60\% & 0.37 & 0.46 & 73.76\% & 35.86\% & 0.35 & 0.37 \\ Avg & 84.79\% & 38.05\% & 0.31 & 0.45 & 90.73\% & 46.28\% & 0.46 & 0.47 \\ \hline  & \multicolumn{4}{c}{Stochastic features a  } \\ \hline AR & 86.69\% & 54.11\% & 0.50 & 0.58 & 86.63\% & 54.03\% & 0.50 & 0.58 \\ GR & 51.69\% & 29.23\% & 0.36 & 0.22 & 51.97\% & 32.01\% & 0.42 & 0.22 \\ Avg & 79.50\% & 53.09\% & 0.49 & 0.57 & 78.75\% & 54.46\% & 0.50 & 0.59 \\ \hline All & 97.51\% & 62.36\% & 0.72 & 0.53 & 98.93\% & 61.80\% & 0.69 & 0.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of classification results (accuracy, sensitivity, specificity) before/after 100% MVC normalization among 40 subjects, matched by age and neck skinfold thickness. Each row corresponds to an individual feature; features are grouped by type; and the average by type is presented at the end of each group. Moreover, the last row shows the overall performance across features. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Actual Positive** & **Actual Negative** \\ \hline
**Predicted Positive** & 97.52\% & 2.51\% \\
**Predicted Negative** & 2.48\% & 97.49\% \\
**Validation Accuracy** & 97.51\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Confusion matrix for the positive vs. negative detection of vocal fatigue among 40 subjects closely matched by age and skinfold thickness, labeled according to the Vocal Fatigue Index factor 1 scores and using leave-one-out cross-validation. As expected, the results indicated that 100% MVC normalization improved classification when using amplitude-dependent features, but it did not affect the frequency-dependent or the stochastic features. However, this test also revealed that: (1) frequency-dependent features were in general much better at detecting fatigued samples demonstrated by their testing accuracy and sensitivity; (2) the more features being added to the classifier, the lesser its ability to generalize to the testing samples.