## 3 Experimental Results and Discussion
Table 6 shows the classification results obtained by the LPCC, skewness, and kurtosis parameters for FNN and CNN. These results were based on the LPCC delta parameter. For the FNN classifier, good accuracy (83.33%) was achieved for classifying elderly and young voices with the LPCC parameter in the case of female data samples. In mixed and male data samples, 78.95% and 81.25% accuracy were achieved by the LPCC and LPCC + kurtosis parameters, respectively. For the CNN classifier, similar results (75.00%) were shown for female and data samples with LPCC + kurtosis and LPCC + skewness + kurtosis, respectively; 63.16% was achieved with LPCC + kurtosis in mixed female and male data samples. For male /a/ vowel data samples, utilization of the FNN classifier, MFCC + delta + skewness, and MFCC + delta + kurtosis parameters obtained the highest accuracy (93.75%) compared with the combination of CNN, FNN, and various parameters. The best accuracy (87.50% and 83.33%) was obtained with MFCC + skewness and the LPCC parameters for the FNN classifier in mixed and female data samples, respectively. Another finding is that the fusion of the skewness and kurtosis parameters had a positive effect on the overall accuracy of the classification. Most results were better with FNN than CNN in all mixed, female, and male data samples. Figure 5 summarizes the performance trends in terms of network, gender, and various parameters. In this figure, it can be seen that FNN performed well in most cases. In terms of gender, the use of male data samples demonstrated good performance with most parameters. Therefore, rather than experimenting with mixed female and male data samples, the author recommends experimenting with and constructing a system that divides the data into female and male individuals. Figure 5c shows that the highest performance was 93.75%, obtained by using the parameters that added skewness and kurtosis to the MFCC and MFCC delta parameters. Of these, the addition of skewness showed the best performance. The figure also shows the better performance of using MFCC \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & LPCC & LPCC + S & LPCC + K & LPCC + SK \\ \hline FNN & & & & & \\ \hline \multirow{3}{*}{/a/} & Women and men & 78.95 \(\pm\) 3.73 & 78.95 \(\pm\) 2.54 & 73.68 \(+\) 3.08 & 76.32 \(+\) 3.55 \\  & Women & 83.33 \(\pm\) 6.24 & 79.17 \(+\) 2.95 & 79.17 \(+\) 3.03 & 79.17 \(+\) 3.02 \\  & Men & 81.25 \(\pm\) 3.23 & 81.25 \(+\) 4.37 & 81.25 \(+\) 6.07 & 81.25 \(+\) 4.22 \\ \hline CNN & & & & & \\ \hline \multirow{3}{*}{/a/} & Women and men & 63.16 \(+\) 3.29 & 63.16 \(+\) 3.88 & 63.16 \(+\) 4.47 & 68.42 \(+\) 4.26 \\  & Women & 66.67 \(+\) 2.81 & 70.83 \(+\) 2.91 & 75.00 \(+\) 4.41 & 70.83 \(+\) 2.36 \\ \cline{1-1}  & Men & 68.75 \(+\) 4.37 & 68.75 \(+\) 4.22 & 68.75 \(+\) 5.47 & 75.00 \(+\) 4.93 \\ \hline \hline \end{tabular}  
\(\mathbb{S}\): skewness; K: kurtosis; SK: skewness and kurtosis.  
\end{table} 
Table 6: Classification results obtained by LPCC, skewness, and kurtosis parameters and the two different deep learning methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **MFCC + Delta & **MFCC + Delta + S** & **MFCC + Delta + K** & **MFCC + Delta + SK** \\ \hline FNN & & & & & \\ \hline \multirow{3}{*}{/a/} & Women and men & 76.32 \(\pm\) 3.47 & 78.95 \(\pm\) 1.58 & 76.32 \(\pm\) 1.94 & 78.95 \(\pm\) 3.54 \\  & Women & 79.17 \(\pm\) 2.95 & 75.00 \(\pm\) 2.95 & 75.00 \(\pm\) 3.61 & 75.00 \(\pm\) 2.94 \\  & Men & 87.50 \(\pm\) 7.10 & 93.75 \(\pm\) 6.59 & 93.75 \(\pm\) 5.15 & 93.75 \(\pm\) 4.37 \\ \hline CNN & & & & & \\ \hline \multirow{3}{*}{/a/} & Women and men & 78.95 \(\pm\) 3.46 & 76.32 \(\pm\) 4.07 & 78.95 \(\pm\) 2.82 & 73.68 \(\pm\) 2.61 \\  & Women & 75.00 \(\pm\) 2.91 & 75.00 \(\pm\) 3.95 & 75.00 \(\pm\) 4.91 & 70.83 \(\pm\) 2.81 \\ \cline{1-1}  & Men & 68.75 \(\pm\) 5.27 & 81.25 \(\pm\) 8.04 & 81.25 \(\pm\) 4.88 & 75.00 \(\pm\) 3.29 \\ \hline \hline \end{tabular}  
\(\mathbb{S}\): skewness; K: kurtosis; SK: skewness and kurtosis.  
\end{table}