## 3 Results and Discussion
### Results of the Analysis of the Classification Model with GRU-Based Training  
According to the results, the CNN showed a relatively high classification performance (97.60% for male and 95.63% for female speakers) compared to others. Figures 9-11 show the accuracy results obtained with and without the proposed GRU-based training method for different hybrid features, window sizes, and window types. Only the CNN was employed as a classifier since it outperformed the others. The results demonstrate that the best performance was achieved with EZDDMFCC (Figure 10). According to the accuracy values presented in Figure 10, the best performances for male and female speakers were obtained with the GRU rectangular window with a size of 20 ms and Hanning window with a size of 35 ms, respectively. Figure 10: EZDMFCC accuracy results without GRU-based training (no dotted bars) and with GRU-based training (dotted bars) using a CNN classifier for male (M) and female (F) speakers. Figure 9: EZMFCC accuracy results without GRU-based training (no dotted bars) and with GRU-based training (dotted bars) using a CNN classifier for male (M) and female (F) speakers. In this study we used various combination parameters, and the models are summarized in Table 11.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multicolumn{11}{c}{**GRU Based Training Model**} \\ \hline \multicolumn{11}{c}{**Male and Female**} \\ \hline \multicolumn{11}{c}{**Hybrid Features**} & \multicolumn{1}{c}{**Classification Types**} & \multicolumn{1}{c}{**Hamming**} & \multicolumn{1}{c}{**Hamming**} & \multicolumn{1}{c}{**Rectangular**} \\ \cline{3-11}  & & **20** & **25** & **30** & **35** & **20** & **25** & **30** & **35** & **20** & **25** & **30** & **35** \\ \hline \multirow{10}{*}{EZMFCC} & CNN & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\  & MLP & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\  & Naive Bayes & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\  & SVM & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\  & Random Forest & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\  & k-NN & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \multirow{10}{*}{EZDMFCC} & CNN & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\  & MLP & \(\surd\) & \Various studies have been carried out to investigate speech segmentation. However, although there are studies conducted with standard datasets created by various universities and institutions in different languages, no comprehensive study to determine the optimum feature parameters based on the GRU for Kurdish speech segment detection has been encountered. The Kurdish vocabulary dataset, which was derived from TRT Kurdi Nace news speech signals, was used to train the classifier with GRU recurrent neural networks. In this study the automatic detection of the C/V/S phoneme segmentation method used for the continuous Kurdish speech segmentation was discussed. The results demonstrate that the suggested method yields promising results for identifying the optimal set of feature parameters for the Kurdish language classifier. According to the obtained analysis results, it was observed that the selection of feature extraction methods and the selection of classifier models in male and female speakers were the two main factors affecting the performance of the Kurdish C/V/S speech detection system.