# Automatic Age and Gender Recognition Using Ensemble Learning
## 5 Discussion
Some studies classify speakers solely based on their gender, while others classify them based on both age and gender. The results given in the table indicate that gender classification accuracy is higher than age classification accuracy. This suggests that classifying age is a more challenging task compared to gender classification. Among the compared studies, there are very few studies in which age and gender classifications are made together. In one of these studies [6], a CNN model with a multiple attention module (MAM) is proposed to classify speakers according to their age and gender. In that study, it is stated that with the proposed model, the speeches in the Korean Speech Recognition dataset were classified into 12 age and gender classes with 90% accuracy. In another study [10], a six-class classification is made according to age and gender, and in that study, it is reported that over 80% accuracy was achieved on the Common Voice dataset with deep neural networks of different types. The class definition used in my previous study [40] is very similar to the class definition in this study. In that study, a 10-class classification defined according to the speaker's age and gender is performed based on 1D and 2D CNN models. The accuracy of the proposed 2D CNN model on the Turkish subsection of the Common Voice dataset is measured as 94.40%. On the other hand, in this study, the number of age and gender classes, which was 10 in the previous study, is increased to 12 with the addition of the seventies female (F70s) and eighties female (F80s) \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Dataset** & **Classifier** & **Number of Classes (Class Labels)** & **Accuracy** \\ \hline
[4] & Voice gender and Common Voice & Backpropagation and bagging & 2 [male, female] & 98.10\% \\  & & & 4 [20s, 40s, 50s, 60s] & 55.39\% \\ \hline
[6] & Korean Speech Recognition & CNN with MAM & 2 [male, female] & 97\% \\  & & & 6 [teens, 20s, 30s, 40s, 50s, 60s] & 97\% \\  & & & 12 [gender x age categories] & 90\% \\ \hline
[7] & TIMIT dataset & Deep Neural Networks & 2 [male, female] & 96.8\% to 99.6\% \\ \hline
[8] & Common Voice & MLP & 10 [male, female, teens, 20s,30s, 40s, 50s, & 89.58\% \\  & & & 60s, 70s, 80s] & 89.58\% \\ \hline
[10] & Common Voice & Deep Neural Networks & 2 [male, female] & above 98\% \\  & & & 6 (YM, YF, AM, AF, SM, SF) & above 80\% \\ \hline
[12] & Common Voice and Sammour & CNN & 2 [male, female] & 94.99\% \\  & & & 3 \{0-15, 20–39, 40+\} & 75.25\% \\ \hline
[13] & Common Voice & ML algorithms & 2 [male, female] & 96.4\% \\  & & & 3 [young, matured, old] & 70.4\% \\ \hline
[22] & Common Voice & RobustScalar, PCA, and LR & 2 [male, female] & 91\% \\  & & & 8 [teens, 20s, 30s, 40s, 50s, 60s, 70s, 80s] & 59\% \\ \hline
[40] & Common Voice & 1D and 2D CNN & 10 [M20s, F20s, M30s, F30s, M40s, F40s, & 94.40\% \\  & & & M50s, F50s, M60s, F60s] & 97.41\% \\ \hline This study & Common Voice & Ensemble model & 12 [M20s, F20s, M30s, F30s, M40s, F40s, & 97.41\% \\  & & & M50s, F50s, M60s, F60s, F70s, F80s] & 97.41\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of the proposed model with state-of-the-art methods. classes. In addition, the dataset used in training the models has also been expanded. In this study, although the number of classes has increased, there is no decrease in classification accuracy and even a 3% increase is achieved. This increase can be considered the result of three factors. The first of these factors may be the use of complementary aspects of different classifiers in the ensemble learning approach. The second factor may be the increase in the amount of data used in the training and testing phases of the models. And the last reason for the increase in accuracy may be the new classes added to the dataset (seventies female and eighties female). In the experiments, it is seen that these two classes are the classes classified with the highest accuracy.