## 4 Experimental Results and Discussion  
In this section, all experiments use speeches from the Turkish subset of the Common Voice dataset, selected based on the criteria detailed in Section 3.1. The dataset is split into two parts with a 75:25 ratio for training and testing. Four classifiers (KNN, LDA, MLP, and CNN) and five feature extraction methods (MFCC, Mel Spectrogram, Chroma, Contrast, and Tonnetz) are utilized in the study. A total of 193 features, including 40 MFCC, 12 Chroma, 128 Mel Spectrogram, 7 Contrast, and 6 Tonnetz, are extracted from each speech signal using the \"librosa\" library in Python during the feature extraction phase. After these features are averaged over time and converted into one-dimensional arrays, they are applied to each classifier, first separately and then stacked end-to-end. The performance evaluation results of the classifiers, created using each feature separately, are presented in Table 3.  
Based on the results in Table 3, it is evident that the CNN classifier consistently offers the highest accuracy in gender recognition, irrespective of the type of feature used. The ranking of classifiers based on accuracy is as follows: CNN, MLP, KNN, and LDA. Additionally, when considering the type of feature, the MFCC feature consistently provides the highest accuracy across all classifiers, followed by the Mel Spectrogram, Contrast, Chroma, and Tonnetz features, in that order. Specifically, the CNN model utilizing MFCC features achieved a remarkable 99.22% classification accuracy by accurately predicting the gender of 1662 out of 1675 speakers in the test dataset. The classification accuracies obtained with other classifiers on the same dataset are as follows: 98.45% with MLP, 98.09% with KNN, and 95.94% with LDA. After examining the gender classification performance of each feature separately, the performance evaluations of hybrid features created by stacking these features in twos, threes, fours, and fives are carried out. Initially, four hybrid features are formed by combining MFCC with four other features. Then, the number of stacked features is increased to three, four, and five to create eight hybrid features, and the performance of each is measured by following the same processes. The study ultimately creates twelve hybrid  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline
**Feature** & **Feature** & \multicolumn{4}{c}{**KNN**} & \multicolumn{4}{c}{**LDA**} & \multicolumn{4}{c}{**MLP**} & \multicolumn{4}{c}{**CNN**} \\ \cline{2-13}
**Type** & **Size** & **Acc. ** & **Prec. ** & **Rec. ** & **F1** & **Acc. ** & **Prec. ** & **Rec. ** & **F1** & **Acc. ** & **Prec. ** & **Rec. ** & **F1** & **Acc. ** & **Prec. ** & **Rec. ** & **F1** \\ \hline MFCC & \(40\times 1\) & 98.09 & 98.09 & 98.09 & 98.09 & 95.94 & 95.98 & 95.94 & 95.94 & 98.45 & 98.45 & 95.45 & 99.22 & 99.23 & 99.22 &  
features by stacking five features in various sequences. A list of these features is given in Table 4, along with the stacked components and their sizes. Each hybrid feature in the table is used as input for four different classifiers, resulting in a total of 48 classification models. Performance evaluations are conducted for these models, and the results are presented in Table 5. The table highlights the highest accuracy achieved by each classifier. When the results in Table 5 are examined, it is seen that a performance increase between 0.3% and 1.73% is achieved in all models created with the use of the hybrid features. The highest classification accuracy is achieved with the CNN model created by stacking the MFCC, Mel Spectrogram, and contrast features (Feature ID = 5). This model improves gender classification accuracy from 99.22% to 99.52% by accurately predicting the gender of 1667 out of 1675 speeches in the test dataset. The CNN model is followed by KNN with an accuracy rate of 99.22%, MLP with an accuracy rate of 99.04%, and LDA models with an accuracy rate of 97.67%. In each model, the highest classification accuracy is achieved with different hybrid features.