# Age Prediction from Korean Speech Data Using Neural Networks with Diverse Voice Features  
[PERSON]  
1 Department of Industrial Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea; [EMAIL_ADDRESS] (H.K. ); [EMAIL_ADDRESS] (M.L. ); [EMAIL_ADDRESS] (S.K. )1 Department of Industrial Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea; [EMAIL_ADDRESS] (H.K. ); [EMAIL_ADDRESS] (M.L. ); [EMAIL_ADDRESS] (S.K. )1 Department of Industrial Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea; [EMAIL_ADDRESS] (H.K. ); [EMAIL_ADDRESS] (M.L. ); [EMAIL_ADDRESS] (S.K. )1 Department of Industrial Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea; [EMAIL_ADDRESS] (H.K. ); [EMAIL_ADDRESS] (M.L. ); [EMAIL_ADDRESS] (S.K. )1 Department of Industrial Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea; [EMAIL_ADDRESS] (H.K. ); [EMAIL_ADDRESS] (M.L. ); [EMAIL_ADDRESS] (S.K. )1 Department of Industrial Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea; [EMAIL_ADDRESS] (H.K. ); [EMAIL_ADDRESS] (M.L. ); [EMAIL_ADDRESS] (S.K. )1 Department of Industrial Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea; [EMAIL_ADDRESS] (H.K. ); [EMAIL_ADDRESS] (M.L. ); [EMAIL_ADDRESS] (S.K. )1 Department of Industrial Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea; [EMAIL_ADDRESS] (H.K. ); [EMAIL_ADDRESS] (M.L. ); [EMAIL_ADDRESS] (S.K. )1 Department of Industrial Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea; [EMAIL_ADDRESS] (H.K. ); [EMAIL_ADDRESS] (M.L. ); [EMAIL_ADDRESS] (S.K. )1 Department of Industrial Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea; [EMAIL_ADDRESS] (H.K. ); [EMAIL_ADDRESS] (M.L. ); tmrf176@konuk. physical age. However, estimating a speaker's age from short-term utterances remains challenging and interest in addressing this issue has recently increased. Early research on speaker age estimation set the foundation for modern approaches. [PERSON], [PERSON] [5] applied an i-vector-based method, demonstrating the feasibility of automated solutions. Subsequently, [PERSON], [PERSON] [6] examined the effects of utterance length and the number of frames on least-squares support-vector regression models, highlighting how speech duration and feature granularity affect predictive accuracy. At around the same time, [PERSON] and [PERSON] [7] introduced a bidirectional gated recurrent neural network (G-RNN) approach that leveraged various features--such as Mel-frequency cepstral coefficients (MFCCs), spectral subband centroids (SSCs), linear-predictive coefficients (LPCs), and formants--to capture the frequency-sensitive elements of speech. [PERSON], [PERSON] [8] further advanced these techniques by demonstrating how acoustic and prosodic feature fusion can bolster model performance. As deep-learning techniques gained momentum, [PERSON], [PERSON] [9] used linear-prediction cepstral coefficients (LPCCs) and Gaussian mixture models (GMMs) to group ages in five-year increments between 5 and 50. Focusing specifically on Korean speech, [PERSON], [PERSON] [10] proposed a deep artificial neural network trained on MFCCs, achieving notable accuracy gains for men in their 20s, 30s, and 50s, as well as for women in their 20s, 40s, and 50s. Further refinements emerged through transformer-based architectures and self-supervised methods, as seen in the work of [PERSON], [PERSON] [11] and [PERSON], [PERSON] [12], both of which reported enhanced predictive accuracy via bi-encoder transformer models and robust speech representations. The most recent wave of research has explored specialized convolutional neural networks (CNNs) and attention mechanisms.