## 3 Analysis and Results of CGAN-IFCM
### Comparison Between CGAN-IFCM and Existing Works  
This subsection discusses the performance of the proposed CGAN-IFCM compared to performance in existing works [12; 13; 14; 15; 16; 17; 18]. Table 9 presents the databases, methodologies, types of cross-validation, and performance under the scenario of the binary detection model for voice disorder detection. Existing works [12; 16; 17] did not apply cross-validation during their performance evaluations, and one can pick up a set of training data and testing data to obtain excellent performance. Therefore, the results in [12; 16; 17] are not comparable to those of the proposed CGAN-IFCM. In comparison with [13; 14; 15] using the SVD database, the proposed CGAN-IFCM improves the TNR and TPR by 12.9-43.5% and 9.1-44.8%, respectively. Further, compared to [18], using VOICED, CGAN-IFCM increases the TNR and TPR by 9.9% and 15.3%, respectively. Moreover, existing works [12; 13; 15; 16; 18] experienced the issue of imbalanced classes, featuring notable deviations between the TNR and TPR from 3.8% to 28.7%. Notably, existing works [12; 13; 14; 15; 16; 17; 18] did not formulate the voice disorder detection problem as one of multi-class detection, thereby precluding a direct comparison. Thus, in this paper, we extend the formulation to the multi-class detection problem for detecting three common types of voice disorders: hyperkinetic dysphonia, hypokinetic dysphonia, and reflux laryngitis. Table 10 summarizes the TNR \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Cases**} & **CGAN-IFCM** & **SMOTE-IFCM** & **CSL-IFCM TNR** & **Percentage Improvement** \\  & **TNR (\%)/TPR (\%)** & **TNR (\%)/TPR (\%)** & **(\%)/TPR (\%)** & **by Proposed Work** \\ \hline Binary detection & \multirow{2}{*}{94.7/95.6} & \multirow{2}{*}{90.1/91.6} & \multirow{2}{*}{89.2/90.3} & \multirow{2}{*}{(4.4–5.1)/(6–6.2)} \\ model with SVD & & & & \\ \hline Binary detection & \multirow{2}{*}{91.6/94.4} & \multirow{2}{*}{87.2/90.1} & \multirow{2}{*}{86.5/89.2} & \multirow{2}{*}{(4.8–5)/(5.8–5.9)} \\ model with SVD & & & & \\ \hline Multi-class detection model & \multirow{2}{*}{92.2/88.9} & \multirow{2}{*}{86.9/85.4} & \multirow{2}{*}{87.5/84.0} & \multirow{2}{*}{(4.1–6.1)/(5.4–5.8)} \\ with SVD & & & & \\ \hline Multi-class detection model & \multirow{2}{*}{89.4/90.1} & \multirow{2}{*}{86.2/86.5} & \multirow{2}{*}{85.0/85.6} & \multirow{2}{*}{(3.7–4.2)/(5.2–5.3)} \\ with VOICED & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance comparison between CGAN-IFCM, synthetic minority oversampling technique (SMOTE)-IFCM, and cost-sensitive learning (CSL)-IFCM. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Work**} & **Database** & **Methodology** &
\begin{tabular}{c} **Types of** \\ **Cross-Validation** \\ \end{tabular} & **Performance** \\ \hline
[12] & SVD (binary class) & SVM and GMM & No & TNR: 99\%; TPR: 94\% \\ \hline
[13] & SVD (binary class) & SMO and SVM & 10-fold & TNR: 83.9\%; TPR: 87.6\% \\ \hline
[14] & SVD (binary class) & LSTM and CNN & 10-fold & Precision of 66–78\% \\ \hline
[15] & SVD (binary class) & SVM & 20-fold & TNR: 78\%; TPR: 72\% \\ \hline
[16] & SVD (binary class) & \
\begin{tabular}{c} Threshold-based \\ detection \\ \end{tabular} & No &
\begin{tabular}{c} TNR: 90.2\%; TPR: 70.6\% \\ TNR: 64.3\%; TPR: 45.8\% \\ \end{tabular} \\ \hline
[17] & VOICED (binary class) & KNN & No & Accuracy of 93.3\% \\ \cline{2-4}  & RF & & Accuracy of 87.4\% \\ \hline
[18] & VOICED (binary class) & Boosted tree & 5-fold & TNR: 86.2\%; TPR: 82.9\% \\ \hline Proposed CGAN-IFCM & SVD (binary class) & CGAN and IFCM & 10-fold & TNR: 94.7\%; TPR: 95.6\% \\ \cline{2-4}  & VOICED (binary class) & & TNR: 91.6\%; TPR: 94.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance comparison between CGAN-IFCM and existing works on the binary detection model for voice disorder detection. and TPR of the proposed CGAN-IFCM with two typical algorithms: RF and SVM (radial basis kernel function).