## 2 Literature Review  
Many studies in the literature have been conducted to automatically classify speakers by gender using various data processing techniques and machine learning methods. This section presents an overview of the current studies on this topic. In [15], a new model was proposed that uses the Deeper Long Short-Term Memory (LSTM) Networks structure for gender prediction from a speech dataset. The proposedmodel achieved 98.4% accuracy in classifying the speeches in a public audio dataset based on the gender of the speakers. In [16], the noise-free smooth data were obtained through data preprocessing. The features were then extracted using a multi-layered architectural model. The study conducted experiments with KNN and SVM classifiers on three different datasets--TIMIT, RAVDESS, and BGC. It was reported that the highest accuracy rate achieved in these experiments was 96.8% in the TIMIT dataset using KNN. In [17], the authors introduced a gender classification model for Arabic speakers based on an ensemble classifier. The model utilized a three-stage machine learning approach to optimize the feature engineering process, resulting in a 96.02% classification rate on the test dataset using the linear SVM classifier. In a different study [18], it was suggested to utilize different Deep Neural Network-based embedder architectures such as x-vector and d-vector for predicting age and gender. The study reported that the model achieved an accuracy of 99.60% on the TIMIT dataset. In a recent study [4], a new convolutional neural network with multiple attention modules was introduced to classify speakers based on their age and gender. The researchers conducted experiments using the Common Voice and local Korean speech recognition datasets. The results showed that the proposed model achieved gender recognition accuracies of 96% and 97% in the respective experiments. In [19], the authors proposed two self-attention-based models for end-to-end gender recognition systems in unconstrained environments. Experimental results from the study showed that the proposed convolution self-attention model for the unconstrained environment provided 96.23% accuracy on the VoxCeleb dataset. In [20], a language-independent classification model for gender recognition was introduced, which utilized the Common Voice dataset. The study involved training a deep learning network to extract vital information from speech spectrograms, followed by fine-tuning the pre-trained ResNet50 model for gender recognition. The proposed model achieved a gender classification accuracy of 98.57%. In [21], a model was proposed for predicting gender from Turkish speech signals by employing MFCC and machine learning. The researchers examined 58 different TV series and movies, creating a new original dataset with 894 audio recordings, each consisting of 5 s segments. The study also involved creating a hybrid feature vector using MFCC and spectrogram features. The researchers analyzed the performance of various machine-learning algorithms such as logistic regression (LR), decision trees (DT), random forest (RF), and extreme gradient boosting (XGB). Among these, logistic regression exhibited the highest accuracy at 89%. In [22], a new method was presented that utilized MFCC, LPC, and LPC features, combining them with KNN and MLPNN classifiers. In the study, the useful coefficients in these three features were combined and used to recognize the gender of the speakers. The experiments were conducted on the TIMIT dataset, and the results showed that the proposed method classified male speakers with an accuracy of 97% and female speakers with an accuracy of close to 98%. In [23], the researchers aimed to develop a gender recognition model with high accuracy and low complexity. To achieve this, a two-stage heterogeneous stacked ensemble model was proposed. In the experiments carried out using data from different sources, the gender recognition accuracy of the proposed model was found to be 99.36%.