## 3 Results and Discussion  
In this study, we used three types of hybrid feature vectors. The first type of hybrid feature vector comprises EZMFCC parameters. The second type of hybrid feature vector comprises EZDMFCC parameters. The third type of hybrid feature vector comprises EZDDMFCC parameters. The above-mentioned types of hybrid features were extracted for each 20, 25, 30, and 35 ms window duration using the Hamming, Hanning, and rectangular windowing functions. All these parameters were used at the feature extraction stage in the speech segment detection. The hybrid feature extraction process produced hybrid feature vectors, which were introduced as an input into the GRU system for learning a pattern and finding an optimal solution for each speech category (C/V/S). After the GRU was trained with the training set, an optimal hybrid feature parameter was found using the EZDDMFCC. Different experiments have been conducted for the C/V/S segment detection with different classifier models. The most common classifier models are CNN, MLP, naive Bayes, SVM, random forest, and k-NN. For the k-NN, the parameter \"\(k\)\" was set to 3. For the MLP, the number of hidden neurons suggested by the empirical selection was 10, 20, 30, 40, and 50 hidden neurons, which were trained separately, and the performance of every neuron was assessed. For the CNN, two convolutional layers with 20 and 100 feature maps with \(5\times 5\) patch sizes and \(2\times 2\) max pooling, respectively, were used. Weka was utilized for obtaining classification accuracies. Accuracy was taken as the performance measure when counting the number of correct C/V/S speech segment detections. The accuracy function was calculated using Equation (16):  
\[\text{Accuracy}=\frac{TP+TN}{TP+FP+FN+TN}, \tag{16}\]  
where _TP_, _TN_, _FP_, and _FN_ are true positive, true negative, false positive, and false negative, respectively. Tables 4-6 present the accuracy results obtained with the proposed GRU-based training method for male speakers, while Tables 7-9 present the accuracy results obtained with the proposed GRU-based training method for female speakers. Figure 8: The architecture framework of a convolutional neural network [53]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Windows**} & \multicolumn{6}{c}{**Classification Type and Performance (\%)**} \\ \hline
**Type** & **Size (ms)** & **CNN** & **MLP** & **Naive Bayes** & **SVM** & **Random Forest** & **k-NN** \\ \hline \multirow{4}{*}{Hamming} & 20 & 97.25 & 91.14 & 82.20 & 89.06 & 93.53 & 90.38 \\  & 25 & 95.77 & 92.05 & 82.28 & 90.32 & 92.14 & 90.44 \\  & 30 & 96.53 & 92.36 & 81.30 & 89.80 & 93.19 & 90.87 \\  & 35 & 97.12 & 91.28 & 82.45 & 89.29 & 92.70 & 90.10 \\ \hline \multirow{4}{*}{Hanning} & 20 & 97.04 & 92.65 & 81.80 & 89.81 & 93.74 & 90.49 \\  & 25 & 96.18 & 92.76 & 81.65 & 89.63 & 93.55 & 91.52 \\  & 30 & 96.60 & 92.13 & 80.28 & 89.04 & 92.00 & 90.24 \\  & 35 & 96.69 & 91.05 & 80.30 & 90.13 & 91.87 & 89.64 \\ \hline \multirow{4}{*}{Rectangular} & 20 & 97.60 & 91.94 & 82.71 & 88.88 & 92.32 & 91.12 \\  & 25 & 96.38 & 92.42 & 81.73 & 89.37 & 93.15 & 90.23 \\ \cline{1-1}  & 30 & 96.22 & 92.08 & 81.45 & 88.17 & 92.03 & 90.61 \\ \cline{1-1}  & 35 & 95.98 & 92.32 & 81.38 & 88.85 & 92.32 & 90.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance accuracy for male speakers based on GRU+EZDDMFCC.