# Age Prediction from Korean Speech Data Using Neural Networks with Diverse Voice Features
## 4 Results and Analyses
### Results  
Tables 3 and 4 present the prediction accuracies for the 10 age groups using the male and female speech datasets, respectively. The hyperparameters for each combination are presented in Appendix A. For males, the ViT model exhibits the best average performance across all four speech features. The top performing input feature, on average, across the four models was MFCCs + \(\Delta\)MFCCs. The best combination of speech features and deep-learning models for predicting male ages was MFCCs + \(\Delta\)MFCCs and the 1D-CNN model, achieving an accuracy of 88.16%. For females, the ViT model also showed the best average performance across all four speech features. The top performing input feature, on average, across the four models was MFCCs. In conclusion, the combination of MFCCs + \(\Delta\)MFCCs and the 1D-CNN model achieved the highest accuracy for predicting female ages, with a result of 81.95%. In summary, ViT demonstrated the best average performance for age prediction in both males and females, and the combination of MFCCs + \(\Delta\)MFCCs and 1D-CNN provided the best results for both sexes. Table 5 provides detailed results for the 1D-CNN and MFCCs + \(\Delta\)MFCCs combination, which achieved an accuracy of 88.16% for male age prediction. It includes specific details on the precision, recall, and F1-score for each age group. Class '11-14' achieved 97% precision, 97% recall, and 97% F1-score. Class '55-59' surpassed the other age groups with 96% precision, 97% recall, and 96% F1-score. Figure 10 illustrates the confusion matrix for the male age prediction results. Table 6 provides detailed results for the 1D-CNN and MFCCs + \(\Delta\)MFCCs combination, which achieved the highest accuracy of 81.95% in the female age prediction. Class '55-59' recorded 95% precision, 94% recall, and 95% F1-score, surpassing other age groups. However, Class '35-39' showed a relatively low prediction performance, with 74% precision, 60% recall, and 66% F1-score. Figure 11 illustrates the confusion matrix for the female age-prediction results, indicating that Class '35-39' was often misclassified as Class '40-44'. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Input Feature** & **Mel** & **log-Mel** & & & \\
**Spectrogram** & **Spectrogram** & **MFCCs** & **MFCCs + \(\Delta\)MFCCs** & **Average** \\ \hline ResNet-50 & 55.31 & 78.45 & 79.64 & 78.19 & 72.90 \\
1D-CNN & 48.47 & 70.89 & 80.78 & 81.95 & 70.52 \\
2D-CNN & 32.15 & 77.91 & 81.07 & 79.33 & 67.62 \\ ViT & 66.20 & 74.74 & 78.43 & 77.75 & 74.28 \\ \hline Average & 50.53 & 75.50 & 79.98 & 79.31 & 71.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Age prediction accuracy (%) for females. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Input Feature** & **Mel** & **log-Mel** & & \\
**Spectrogram** & **Spectrogram** & **MFCCs** & **MFCCs + \(\Delta\)MFCCs** & **Average** \\ \hline ResNet-50 & 66.05 & 85.73 & 87.00 & 87.23 & 81.50 \\
1D-CNN & 65.83 & 82.02 & 87.05 & 88.16 & 80.77 \\
2D-CNN & 61.11 & 85.44 & 86.80 & 85.89 & 79.81 \\ ViT & 78.84 & 86.36 & 85.15 & 85.55 & 83.98 \\ \hline Average & 67.96 & 84.89 & 86.50 & 86.71 & 81.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Age prediction accuracy (%) for males. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Age Group** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline
11–14 & 0.79 & 0.94 & 0.86 & 797 \\
15–19 & 0.87 & 0.78 & 0.82 & 1000 \\
20–24 & 0.88 & 0.85 & 0.87 & 991 \\
25–29 & 0.76 & 0.83 & 0.79 & 999 \\
30–34 & 0.77 & 0.80 & 0.78 & 997 \\
35–39 & 0.74 & 0.60 & 0.66 & 990 \\
40–44 & 0.74 & 0.74 & 0.74 & 983 \\
45–49 & 0.80 & 0.81 & 0.80 & 993 \\
50–54 & 0.89 & 0.92 & 0.91 & 998 \\
55–59 & 0.95 & 0.94 & 0.95 & 996 \\ \hline Weighted avg. & 0.82 & 0.82 & 0.82 & 9744 \\ \hline Accuracy & \multicolumn{4}{c}{0.8195} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Detailed performance for female age prediction using 1D-CNN with MFCCs + \(\Delta\)MFCCs.