## 2 Literature Review  
Many speaker recognition methods were proposed by researchers, such as text-dependent or text-independent systems and speaker identification or speaker verification systems that use pitch or other features as system inputs. The authors in [6] developed a voice recognition methodology that depends on the acoustic signal of the vocal cords' vibration to identify the person. The methodology works through pre-processing and feature extraction steps based on a new measurement technique for the vocal cords' vibration frequency, unlike the existing strategy that deals with the speaker's voice. The methodology provides 91.00% accuracy in identifying the desired speaker. [PERSON] et al. [7] proposed a speaker identification system with a normalized pitch frequency (NPF) incorporated with mel frequency cepstral coefficients (MFCCs) as feature input for improving the speaker identification system's performance. The MFCCs were extracted as a feature of the system, and then a normalization step for the pitch detection approach was implemented. After feature extraction, the proposed speaker identification system was evaluated with an artificial neural network (ANN) using a multi-layer perceptron (MLP) model and classifying the speaker based on the new proposed feature NFP incorporated with MFCCs. The experiment includes seven cases of feature extraction. Simulation results show that the proposed system improves the recognition rate. [PERSON] et al. [8] used two prototypical deep convolutional neural networks (CNNs): the visual geometry group (VGG-CNN) and residual networks (ResNet) and selected the VoxCeleb dataset to evaluate the proposed system. The authors detailed the network training parameters with varying learning rates to warm up the learning process. They fixed the length of the input sequence up to 3 s. They compared the state-of-the-art methods to evaluate the proposed approach's effectiveness, such as traditional i-Vector-based methods, ResNet with and without the proposal self-attention layer, and VGG like CNN with and without the proposed self-attention layer. The result for the speaker identification system shows that the proposed two methods reached 88.20% and 90.80% top 1 accuracy, respectively. Moreover, [PERSON] et al. [9] proposed a speaker identification system in an emotional state using CNN and long short-term memory (LSTM) to design a convolutional recurrent neural network (CRNN) system. They selected the King Saud University Emotion (KSUE-motion) corpus for Modern Standard Arabic (MSA) and the Emotional Prosody Speech and Transcripts (EPST) corpus for the English language. The experiment investigated the impact of language variation and the speaker's emotional state on system performance. For the pre-processing step, the unvoiced and silent part of the speech signal was ignored. The proposed system recognizes speakers' regard for their emotional state. The accuracy of the experiment shows a good performance with 97.46% for the Arabic language, 97.18% for the English language, and 91.83% for cross-language, which is highly promising considering that only one input feature was used in the system, which is the spectrogram. [PERSON] et al. [10] evaluated a novel fusion of MFCCs and time-based features, which were combined to improve the accuracy of the speaker identification system. The system was developed by a feedforward neural network (FFNN). A customized FFNN was used as a classifier to identify the speaker. To train the FFNN, the MFCCs feature was fed to identify speakers based on the utterance's unique pattern. To avoid overfitting, different training functions were used. The experimental results for the proposed MFCCT showed overall accuracy between 83.50% and 92.90%. [PERSON] et al. [11] recently evaluated a text-independent speaker recognition system using deep CNN. They experimented with two comprehensive architectures: ResNet and VGG and selected the VoxCeleb1 dataset for the proposed system.