## 4 Experimental Results and Discussion
The CNN and KNN model achieved the highest classification accuracy by using a stacking of the MFCC, Mel Spectrogram, and contrast \begin{table}
\begin{tabular}{l l l} \hline \hline
**Feature ID** & **Stacked Features** & **Feature Size** \\ \hline
1 & MFCC + Chroma & \(52\times 1\) \\
2 & MFCC + Mel Spectrogram & \(168\times 1\) \\
3 & MFCC + Contrast & \(47\times 1\) \\
4 & MFCC + Tonnetz & \(46\times 1\) \\
5 & MFCC + Mel Spectrogram + Contrast & \(175\times 1\) \\
6 & MFCC + Chroma + Mel Spectrogram & \(180\times 1\) \\
7 & MFCC + Mel Spectrogram + Tonnetz & \(174\times 1\) \\
8 & Chroma + Mel Spectrogram + Contrast & \(147\times 1\) \\
9 & MFCC + Chroma + Mel Spectrogram + Contrast & \(187\times 1\) \\
10 & MFCC + Chroma + Mel Spectrogram + Tonnetz & \(186\times 1\) \\
11 & Chroma + Mel Spectrogram + Contrast + Tonnetz & \(153\times 1\) \\
12 & MFCC + Chroma + Mel Spectrogram + Contrast + Tonnetz & \(193\times 1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hybrid features created by stacking five different features. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Feature** & \multicolumn{3}{c}{**KNN**} & \multicolumn{3}{c}{**LDA**} & \multicolumn{3}{c}{**MLP**} & \multicolumn{3}{c}{**CNN**} \\ \cline{2-13}
**ID** & **Acc. ** & **Prec. Rec. ** & **F1** & **Acc. ** & **Prec. ** & **Rec. ** & **F1** & **Acc. ** & **Prec. ** & **Rec. ** & **F1** & **Acc. ** & **Prec. ** & **Rec. ** & **F1** \\ \hline
1 & 98.20 & 98.22 & 98.21 & 98.21 & 96.24 & 96.27 & 96.24 & 96.24 & 98.93 & 98.93 & 98.93 & 99.28 &  
features (ID = 5). The MLP model achieved the highest classification accuracy by using a stacking of the MFCC and contrast features (ID = 3). The LDA model achieved the highest classification accuracy by using a stacking of the MFCC, Chroma, Mel Spectrogram, and contrast features (ID = 9). Among these three features, the size of the one created by stacking the MFCC and contrast is considerably lower (47 \(\times\) 1) than the size of the other two (175 \(\times\) 1 and 187 \(\times\) 1). Despite this, an increase of between 0.18% and 1.59% is achieved in the accuracy of the models developed with this feature. In addition, the difference between the accuracy of this model (%99.40) and the highest accuracy (%99.52) obtained in the study is only 0.12%. Taking this into account, it can be concluded that the hybrid feature created by stacking MFCC and contrast features is the most effective feature for the proposed gender classification model. To thoroughly assess the performance of the gender classification models developed in the study, their confusion matrix is presented in Table 6.  
The confusion matrix in Table 6 illustrates the distribution of the correct and incorrect predictions made by each classifier based on gender classes. The CNN accurately predicts the gender class of 1667 speakers, while the KNN, MLP, and LDA correctly predict the gender class of 1662, 1659, and 1636 speakers, respectively. The numbers of incorrectly predicted samples by these models are as follows: CNN (8 speakers), KNN (13 speakers), MLP (16 speakers), and LDA (39 speakers). When considering the accuracy of these classifiers as a percentage for each class separately, we find that the CNN model has an accuracy of 99.17% for female speakers and 99.88% for male speakers. The KNN model achieves 99.53% accuracy for female speakers and 98.91% for male speakers, while the MLP model scores 99.06% for female speakers and 99.03% for male speakers. Finally, the LDA model has an accuracy rating of 98.35% for female speakers and 96.98% for male speakers. The experimental results from this study are compared with similar studies on the subject in Table 7. Most of these studies used different datasets, so it is important to consider the datasets used when comparing the results. The Common Voice dataset used in this study contains vocalizations recorded online by volunteer users in uncontrolled environments.