# Age Prediction from Korean Speech Data Using Neural Networks with Diverse Voice Features
## 5 Conclusions  
In this study, four different speech feature extraction methods and four neural network architectures were employed to predict the ages of Korean male and female speakers aged between their teens and 50s, with the goal of identifying the optimal age-prediction combination specific to Korean speech. While many studies have explored predicting age from speech, few have focused on Korean speech, and most either lack specific age ranges or do not segment the data into distinct age groups. To address these limitations, this study used a large Korean dataset to predict ten segmented age groups. The speech features used were the Mel spectrogram, log-Mel spectrogram, MFCCs, and MFCCs + \(\Delta\)MFCCs, while the deep learning models comprised ResNet-50, 1D-CNN, 2D-CNN, and ViT. Among the four speech features, the model that achieved the highest average accuracy was ViT. For all combinations, the age prediction performance for males surpassed that for females. For both males and females, the highest accuracy was obtained when MFCCs + \(\Delta\)MFCCs was trained on 1D-CNN, with accuracies of 88.16% and 81.95%, respectively. This combination represented the optimal pairing of speech features and models specialized for the Korean speech dataset presented in this study. Figure 13: Comparison of age-prediction accuracy across different input features by model for (**a**) male and (**b**) female data. While this study employed established deep learning architectures, there remains significant potential for developing novel architectures that are specifically optimized for speech age prediction. Future research could focus on specialized attention mechanisms that more effectively capture age-specific vocal characteristics. Furthermore, exploring hybrid architectures that integrate the strengths of CNNs and transformers may enhance model performance, whereas investigating self-supervised learning approaches could leverage unlabeled speech data more efficiently. For female speakers, further analysis is recommended to identify the underlying factors that make distinguishing voices of those in their late 30s from those in their early 40s more challenging. The model could also be extended to perform multitask learning, allowing simultaneous recognition of age, emotions, place of origin, and other characteristics. Nevertheless, our current framework achieves state-of-the-art performance for fine-grained Korean speech age prediction while maintaining practical applicability, demonstrating significant improvements over existing approaches. The achievement of 88.16% accuracy across ten distinct age groups represents a substantial advance in the field, particularly given the challenging nature of distinguishing between narrowly separated age ranges. The results of this study can be applied to personalized services designed for Korean speech or services sensitive to narrow age ranges. The model trained on the optimal combination demonstrated strong performance in predicting 10 segmented age groups by using short speech samples. This suggests the feasibility of automated age prediction for large, unspecified populations with potential applications in various fields. Although applying speech data from different languages or cultural backgrounds may not guarantee the same level of performance, the preprocessing techniques and framework presented in this study offer a valuable reference for the development of more advanced models and applications in speech recognition and age prediction. Looking ahead, this approach presents promising potential to further improve automated speech systems across diverse linguistic and cultural contexts. Nonetheless, several open issues remain, such as further investigating age-related changes beyond the 50s, addressing the scarcity of high-quality labeled data in other dialects and languages, and exploring advanced techniques (e.g., self-supervised or domain-adaptive methods) to enhance model robustness.