# Automatic Age and Gender Recognition Using Ensemble Learning
## 2 Related Works  
[PERSON] et al. [8] discussed predicting the age and gender classes of the speaker from the speech signal using multiLayer perceptron (MLP) architecture. In the study, features such as MFCCs, pitch, formants, and chroma were extracted from each speech signal and then selected using the principal component analysis and redundant feature elimination techniques. In the tests performed on the Mozilla Speech database, the accuracy of the developed model was measured as 89.58%. In another study, [PERSON] et al. [6] proposed a new end-to-end age and gender recognition model based on CNN with multiple attention module (MAM). In the study, the multiple attention module was used to extract salient features from the input. The proposed model was tested with Common Voice and a local Korean speech dataset. In these tests, the proposed model's gender, age, and age-gender classification accuracies on the Common Voice dataset were 96%, 73%, and 76%, while its accuracies on the Korean speech dataset were 97%, 97%, and 90%, respectively. In their study, [PERSON] and [PERSON] [7] applied various deep neural network-based embedding architectures such as x-vector and d-vector for the task of age estimation and gender classification. The system with the best performance reached a mean absolute error (MSE) rate of 5.12 for males and 5.29 for females in age estimation and 99.60% accuracy in gender recognition. In another study, [PERSON] et al. [10] analyzed the performance of different types of deep neural networks to jointly determine the age and gender classes of the speaker from the speech signal. The study examined convolutional neural networks and the networks that use historical information such as recurrent convolutional neural networks and temporal convolutional networks. In experiments conducted with the Mozilla Common Voice dataset, error rates of below 2% in gender classification and below 20% in age classification were achieved. In [13], different prediction models were created to predict the age, gender, and emotion classes from audio clips, and the test accuracies of each model were compared. In the study, the highest gender prediction accuracy was measured as 96.4% with the CatBoost model, and the highest age prediction accuracy was measured as 70.4% with the random forest model. In another study, [PERSON] et al. [4] proposed a novel system to classify speech based on the gender, age, and accent of the speakers using machine learning based on an adaptive backpropagation and bagging algorithm. In the experiments, the gender classification accuracy of the adaptive backpropagation algorithm was 98% and the accuracy of the bagging algorithm was 98.10%, while the highest accuracy in age classification was 55.39% with the bagging algorithm. In the study of [PERSON] et al. [22], two different models were proposed for age and gender predictions. While a sequential model with five hidden layers was used for gender prediction, PCA and logistic regression were used as a pipeline for age prediction. In experiments conducted on the Common Voice dataset, an accuracy of around 91% for gender and 59% for age was achieved. In another study, [PERSON] et al. [12] proposed a CNN model for age and gender recognition from speech signals. In the study, variability was added to the training set by masking at random frequencies on the spectrograms, and thus the generalizability of the model was improved. In the study, the gender classification accuracy of the proposed model was reported as 94.99% and the age classification accuracy was reported as 75.24%.