## 2 Materials and Methods
### Proposed Model  
In this study, the focus was placed on the recently suggested gated recurrent unit (GRU)-based model, the research on which had not been conducted for Kurdish speech before this study. The RNN was customized for speech segment detection based on the discrimination between consonants, vowels, and silence. Figure 1 shows the general steps of the proposed architecture for the Kurdish C/V/S speech segment detection. The training dataset contained C/V/S speech data that was tagged manually. The gender detection step identified the gender of a speaker from these tagged speech signals. In the frame blocking and windowing step, the tagged speech signals were segmented into small duration blocks of 20, 25, 30, and 35 ms known as frames. After that, each frame was multiplied with a Hamming, Hanning, or rectangular window function. In the hybrid feature extraction step, each windowed frame feature was obtained using \"energy, ZCR, MFCC (EZMFCC)\" or \"energy, ZCR, DD-MFCC (EZDMFCC)\" or \"energy, ZCR, DD-MFCC (EZDMFCC)\" techniques. In the hybrid feature vectors step, 15-, 28-, or 41-dimensional hybrid feature vectors were generated per frame. In the training with the GRU step, each different dimensional hybrid feature vectors were given to the GRU for learning a C/V/S pattern. The GRU computation was performed by the gated recurrent unit, which modulates information inside the unit without having a separate memory cell. It combines the \"forget gate\" and \"input gate\" into a single \"update gate\" and has an additional \"reset gate.\"  
A GRU network accepts an input sequence \(x=(x_{1};\ldots;x_{t})\). Each \(x_{t}\) represents a hybrid feature frame vector during the training process. The hybrid feature frame vector training set is introduced into the network as a GRU block series. Each \(j\).GRU block was computed in evolutionary terms based on the following equations in an iterative way from \(t=1\) to \(T\). At time \(t\), the activation of the \(j\).GRU output is the \(h_{t}j\).GRU unit. Figure 1: Flow chart of the proposed system. GRU: gated recurrent unit. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Female Speakers** & **Phonemes** & **Vowel Phonemes** & **Consonant Phonemes** \\ \hline Female 1 & 780 & 326 & 454 \\ Female 2 & 1410 & 615 & 795 \\ Female3 & 774 & 341 & 433 \\ \hline Total & 2964 & 1282 & 1682 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Dataset from female speakers. The activation \(h_{t}{}^{j}\) of the GRU at time \(t\) is a linear interpolation between the previous activation \(h_{t-1}{}^{j}\) and the candidate activation \(\overline{h}_{t}{}^{j}\) are represented in Equations (1) and (2):  
\[\overline{h}_{t}{}^{j}=tanh\Big{(}W\cdot\Big{[}r_{t}{}^{j}*h_{t-1}{}^{j},x_{t} \Big{]}\Big{)}, \tag{1}\]  
where \(r_{t}{}^{j}\) refers to a set of reset gates, and \(*\) refers to an element-wise multiplication. \[h_{t}{}^{j}=\Big{(}1-z_{t}{}^{j}\Big{)}h_{t-1}{}^{j}+z_{t}{}^{j}\overline{h}_{ t}{}^{j}. \tag{2}\]  
The update gate \(z_{t}{}^{j}\) decides how much of an update of its activation the unit performs and checks how much the past state must mean at the moment. There will be active reset gates \(r\) in units having short-term dependencies, while there are active update gates \(z\) in units having long-term dependencies. The update gates are given as Equation (3):  
\[z_{t}{}^{j}=\sigma(W_{Z}x_{t}+U_{Z}h_{t-1}){}^{j}, \tag{3}\]  
In case of the off reset gate (\(r_{t}{}^{j}=0\)), it ensures that the unit forgets the past. This is similar to allowing the unit to read the first symbol of an input sequence, where \(\sigma\) is the sigmoid function. \(W_{z}\) is the weight vector for the update gate, and \(W_{r}\) is the weight vector for the reset gate. The reset gate is computed using Equation (4):  
\[r_{t}{}^{j}=\sigma(W_{r}x_{t}+rh_{t-1}){}^{j}. \tag{4}\]  
Hybrid feature frame vectors were utilized by GRU blocks in order to strongly match C/V/S patterns. Figure 2 describes the proposed GRU-trained model.