## 3 Experimental Results and Discussion  
A histogram is an approximate representation of the distribution of numerical data. To configure a histogram, the first step is to create the value range of a \"bin\". That is, it divides the entire range of values into a series of intervals and then calculate the number of values contained in each interval [36]. A histogram can be thought of as simple kernel density estimates that smooth the frequency of bins by using kernels. This usually results in a smoother probability density function that more accurately reflects the distribution of the underlying variable. The density estimate can be displayed instead of a histogram, and is usually drawn as a curve rather than a set of boxes [36]. Figure 1 shows the histograms and curves of MFCC data extracted from elderly and young voices to provide a better visualization of elderly and young voice signals from men and women for the /a/ vowel. In Figure 1a, overall similar tendencies are observed in both older and younger voice signals in both the histograms and curves of MFCC for female /a/ vowel samples. As shown in Figure 1b,c, the histograms and curves of young voice signals are more concentrated near zero than older voice signals for male and mixed /a/ vowel samples. Figure 2 shows the histograms and curves of LPCC data extracted from elderly and young voice signals of men and women for the /a/ vowel. In Figure 2a, the histogram and curve estimated for elderly voice signals tend to show a similar pattern compared to those extracted from young voice signals. In contrast, the histogram and curve extracted from elderly voice signals tend to be slightly more distributed near zero than those extracted from young voice signals, as shown in Figure 2b. A similar tendency towards symmetry is observed for both older and younger voice signals in both the histograms and curves of LPCC for mixed female and male /a/ samples. In Figures 1 and 2, the distribution of MFCC and LPCC data for female, male, and mixed sampled have similar patterns. Figure 3 shows the histograms and curves of skewness extracted from elderly and young voice signals by men and women for the /a/ vowel. In female /a/ vowel samples (Figure 3a), the skewness extracted from elderly voice signals tends to be distributed above zero. There is a tendency to be positively skewed with moderate skewness. However, the skewness extracted from young voice signals tends to be distributed near and below zero compared with elderly voice signals. That is, there seems to be a fairly symmetrical and negatively skew with a left tail. In male /a/ vowel samples (Figure 3b), similar tendencies can be observed for both elderly and young voice signals in both the histograms and curves, but the histogram and curve of skewness extracted from young voice signals have a longer distribution and larger values. There seems to be a fairly symmetrical and moderate skew in both elderly and young voice signals. Overall, in Figure 3a,b, it can be seen that the skewness extracted from female voice signals is distributed above zero compared with the skewness extracted from male voice signals. Clearly, the skewness distribution in the two figures \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & \\ \hline Activation function & ReLU & \\ Kernel size & & (2, 2) \\ Optimizer & Adam & \\ Epoch & 100 & \\ Loss function & Cross-entropy & \\ Dropout & & 0.05 \\ Pooling window & & Max-pooling (2, 2) \\ Neurons at the dense layer & & 512 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of CNN. shows a different pattern. In the mixed female and male /a/ vowel samples (Figure 3c), the histogram and curve patterns also have a distinctly different pattern compared to female and male /a/ vowel samples. In female /a/ vowel samples (Figure 4a), the histograms and curves of young voice signals tend have a longer kurtosis distribution, and the kurtosis estimated for elderly voice signals tends to be shorter and wider than that of young voice signals.