# Age Prediction from Korean Speech Data Using Neural Networks with Diverse Voice Features
[PERSON], [PERSON] [3] introduced a CNN with a multi-attention module (MAM) on speech spectrograms generated via short-time Fourier transform (STFT), showing that accurate age classification is possible, even across multiple languages and varying age brackets. [PERSON], [PERSON] [13] compared seven self-supervised learning (SSL) models for joint age estimation and gender classification on the TIMIT corpus and demonstrated that an attention-based prediction model outperformed wav2 vec 2.0 in both clean and 5 dB signal-to-noisy conditions, achieving more robust and accurate speech representations. However, several studies, including those of [PERSON] and [PERSON] [1] and [PERSON], [PERSON] [14], emphasize that domain-specific adaptations--particularly the integration of diverse feature sets--are crucial for capturing the full breadth of language- and culture-specific vocal nuances. However, these studies have several limitations that create significant practical challenges. First, studies using non-Korean datasets cannot be directly applied to Korean speakers because of language-specific vocal characteristics. The unique features of Korean speech, such as pitch patterns and consonant tensing, significantly affect age-related voice characteristics, leading to poor performance when models trained on other languages are used. Second, the few existing studies on Korean speech either lack comprehensive age coverage or use broad and imprecise age categories that limit practical applications. This makes it difficult to provide accurate age-specific services, such as precise content recommendations or customer-service matching. Finally, most studies have relied on single-feature extraction methods or limited model architectures, potentially missing important vocal characteristics specific to Korean speakers. This approach fails to capture the complex interactions between various acoustic features unique to Korean age-related speech patterns, resulting in suboptimal real-world performance. To address these limitations, this study proposes a comprehensive framework that is specifically designed to overcome each challenge. For a robust analysis of Korean speech characteristics, we utilized a large-scale dataset, comprising approximately 2200 speech samples per age, collected from various regions of Korea to ensure a balanced representation across different dialects and speaking styles. To enable precise age-specific services, we implemented fine-grained classification in five-year increments across the ages of 11-59, resulting in 10 distinct age groups. To capture various age-related voice characteristics, we employed complementary feature-extraction methods (Mel spectrogram and log-Mel spectrogram for frequency-domain analysis, MFCCs and \(\Delta\)MFCCs for temporal dynamics) and analyzed the characteristics using various deep-learning architectures (ResNet-50, 1D-CNN, 2D-CNN, and a vision transformer). Notably, we trained all models from scratch rather than using pre-trained models because existing pre-trained models optimized for general speech or other languages may not effectively capture unique Korean phonological characteristics [15,16]. This study makes three major contributions to existing literature. First, we established an effective framework for Korean speech-based age prediction by systematically evaluating various combinations of voice features and deep-learning models. Our experiments showed that the combination of MFCCs + \(\Delta\)MFCCs features with a 1D-CNN architecture achieves the best performance for both sexes. Second, we demonstrated superior classification accuracy (88.16% for males and 81.95% for females), while using more detailed age groups than in previous studies, enabling more precise age-sensitive applications, such as personalized customer-service matching. Finally, through a detailed performance analysis, we identified specific challenges in age prediction, particularly in distinguishing voices in the 30-39 age range, providing crucial insights for future research in Korean speech processing.