# Age Prediction from Korean Speech Data Using Neural Networks with Diverse Voice Features
## 4 Results and Analyses
### Comparative Analyses
#### 4.3.3 Feature Extraction and Model Performance  
Another observation is that, across all input features, MFCCs and MFCCs + \(\Delta\)MFCCs consistently provided the highest accuracy for both sexes, particularly when used with the 1D-CNN model. This consistent result across features and sex groups emphasizes the robustness of MFCC-based features in capturing age-related information in Korean speech, regardless of sex. However, the Mel spectrograms, especially for female speakers, yielded lower accuracies. This suggests that the Mel spectrogram may lack the detail necessary for accurately capturing age-related differences in vocal characteristics, especially when compared with more nuanced features, such as MFCCs or the log-Mel spectrogram. The findings underscore not only the sex-specific patterns in age prediction accuracy but also the essential importance of selecting feature extraction methods attuned to the linguistic and demographic nuances of the dataset. Notably, the consistent accuracy of MFCC-based features across the sexes indicates their efficacy in capturing the age-related characteristics of Korean speech. This result suggests that tailoring feature extraction approaches to a dataset's linguistic characteristics and demographic distinctions can enhance predictive performance, especially in capturing nuanced age progression within specific sex groups. Figure 12 shows the prediction accuracy of each model based on the input features. In the male age prediction results, when comparing the performance across input features, the Mel spectrogram showed a significantly lower average performance than the other input features. The log-Mel spectrogram, which applies a logarithmic scale to the Mel spectrogram, demonstrated higher performance, suggesting that the Mel spectrogram is less effective at distinguishing the characteristics of male and female voices. The log-Mel spectrogram achieved the highest performance with ViT for male age prediction and ResNet-50 for female age prediction. The MFCCs performed best with a 1D-CNN for males and a 2D-CNN for females. For both sexes, the best performance was obtained when training a 1D-CNN with MFCCs + \(\Delta\)MFCCs. Figure 13 shows the prediction accuracy of each input feature by model. ResNet-50 and 2D-CNN achieved the best performance for both males and females when trained with MFCCs, while 1D-CNN performed best when using MFCCs + \(\Delta\)MFCCs. For ViT, the best performance was achieved using the log-Mel spectrogram for males and MFCCs for females. The models trained with Mel spectrograms--ResNet-50, 1D-CNN, and 2D-CNN--achieved an accuracy of up to 66.05% for male age prediction and up to 55.31% for female age prediction. In contrast, ViT achieved higher accuracies: 78.84% for males and 66.20% for females. This demonstrates that ViT is a robust model that is capable of capturing key features, even in data where feature identification is challenging. Although transformers, including ViTs, have demonstrated remarkable performance across various computer vision tasks, they inherently lack certain inductive biases found in CNNs. CNNs are designed with the assumption that images or speech signals contain rich and localized information, which allows them to effectively capture regional details through spatial hierarchies. This attribute is advantageous for tasks such as age prediction, in which local patterns in voice data can reveal subtle age-related features. In contrast,  
Figure 12: Comparison of age prediction accuracy across different models by input feature for (**a**) male and (**b**) female data. ViT divides images into small patches and uses self-attention mechanisms to capture the context among patches, thereby facilitating comprehensive feature interaction across the input. However, this approach may not emphasize local structures as effectively as a CNN. Although ViT incorporates positional embeddings and self-attention to learn the global context, it may overlook the fine-grained details that are essential for tasks that rely on localized patterns.