## 3 Experimental Results and Discussion
However, in terms of kurtosis, the histograms and curves for both voice signals tend to have relatively flat peaks and lighter tails than a normal distribution. On the other hand, in case of male /a/ vowel samples (Figure 4b), the kurtosis estimated in elderly voice signals tends to be longer and wider than that of young voice signals. Because the histograms of the kurtosis estimated for elderly voice signals tends to be less than zero, the tendency has relatively flat peaks and lighter tails than a normal distribution. In the case of the histograms of the kurtosis extracted from young voice signals, some of them tend to have values greater than zero. Therefore, it can be seen that some of them have sharp peaks and heavier tails than a normal distribution, and most of them have relatively flat peaks and lighter tails. In the mixed female and male /a/ vowel samples (Figure 4c), the histogram and curve have a different pattern to those in Figure 4a for both elderly and young voice signals. Table 4 shows the classification results obtained by the MFCC, skewness, and kurtosis parameters and by the deep learning methods (FNN and CNN). In Table 4, for the FNN classifier, when using male /a/ vowel samples, the combination of MFCC, skewness, and kurtosis showed the best performance (87.50%) for classifying elderly and young voices. In the mixed data samples from women and men, an average accuracy of 87.50% was also obtained by using MFCC + skewness. In addition, the average performance was 75.00% for female data samples with the MFCC parameter. For the CNN classifier, the best accuracy (87.50%) for classifying elderly and young voices was obtained with the combination of MFCC and skewness in the case of male data samples; in mixed and female data samples, 78.95% and 75.00% accuracy were obtained with the MFCC + skewness + kurtosis and MFCC + skewness parameters, respectively. Figure 1: Histograms of the 20-dimensional MFCCs extracted from elderly and young voices. Figure 3: Histograms of skewness extracted from elderly and young voices. Figure 2: Histograms of the 20-dimensional LPCCs extracted from elderly and young voices. Table 5 shows the classification results obtained by the MFCC delta, skewness, and kurtosis parameters for FNN and CNN. These results were based on the MFCC delta parameter. In Table 5, utilization of the FNN classifier with the MFCC + delta + skewness parameters obtained the highest accuracy (93.75%) in male /a/ vowel samples compared with the other classifier and parameter combinations. Additionally, 78.95% and 79.17% accuracy were obtained, respectively, by the MFCC + delta + skewness + kurtosis and MFCC + delta parameters with the FNN classifier for mixed and female data samples. For the CNN classifier, 78.95%, 75.00%, and 81.25% accuracy was achieved with MFCC + delta, MFCC + delta+ kurtosis, and MFCC + delta + skewness for the mixed, female, and male data samples, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **MFCC** & **MFCC + S** & **MFCC + K** & **MFCC + SK** \\ \hline FNN & & & & & \\ \hline \multirow{2}{*}{/a/} & Women and men & 81.58 \(\pm\) 3.30 & 87.50 \(\pm\) 2.31 & 78.95 \(\pm\) 2.07 & 81.58 \(\pm\) 3.72 \\  & Women & 75.00 \(\pm\) 4.12 & 75.00 \(\pm\) 3.67 & 75.00 \(\pm\) 2.95 & 75.00 \(\pm\) 3.61 \\  & Men & 81.25 \(\pm\) 3.02 & 87.50 \(\pm\) 3.23 & 87.50 \(\pm\) 3.28 & 87.50 \(\pm\) 3.95 \\ \hline CNN & & & & & \\ \hline \multirow{2}{*}{/a/} & Women and men & 78.95 \(\pm\) 3.73 & 73.68 \(\pm\) 2.22 & 73.68 \(\pm\) 2.71 & 78.95 \(\pm\) 6.35 \\  & Women & 70.83 \(\pm\) 3.54 & 75.00 \(\pm\) 3.95 & 70.83 \(\pm\) 4.39 & 75.00 \(\pm\) 2.81 \\ \cline{1-1}  & Men & 81.25 \(\pm\) 3.55 & 87.50 \(\pm\) 6.87 & 81.25 \(\pm\) 7.33 & 75.00 \(\pm\) 5.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification results obtained by MFCC, skewness, and kurtosis parameters and the two different deep learning methods. Figure 4: Histograms of kurtosis extracted from elderly and young voices.