# Age Prediction from Korean Speech Data Using Neural Networks with Diverse Voice Features
## 2 Data and Feature Extraction
### Feature Extraction  
Feature extraction is the process of converting a raw speech signal into acoustic feature vectors that capture the specific characteristics of the speaker [18]. The performance and prediction accuracy can vary, depending on the method used for extracting the features from the speech signal [19]. All audio in this study was sampled at a rate of 16 kHz and consists of 3 s segments. Features frequently used in speech and emotion recognition, such \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Age**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Validation**} & \multicolumn{2}{c}{**Testing**} \\ \cline{2-7}  & **Male** & **Female** & **Male** & **Female** & **Male** & **Female** \\ \hline
11–14 & 6756 & 7637 & 764 & 860 & 799 & 797 \\
15–19 & 9308 & 8535 & 1013 & 922 & 987 & 1000 \\
20–24 & 8822 & 8833 & 1000 & 1034 & 982 & 991 \\
25–29 & 8993 & 9004 & 979 & 980 & 999 & 999 \\
30–34 & 8859 & 8908 & 1012 & 1041 & 984 & 997 \\
35–39 & 8867 & 8942 & 1025 & 977 & 982 & 990 \\
40–44 & 8897 & 8800 & 1015 & 998 & 990 & 983 \\
45–49 & 8884 & 8370 & 1011 & 926 & 991 & 993 \\
50–54 & 8907 & 8987 & 950 & 975 & 989 & 998 \\
55–59 & 8987 & 9012 & 929 & 957 & 991 & 996 \\ \hline Total & 87,280 & 87,028 & 9698 & 9670 & 9694 & 9744 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Composition of the training, validation, and testing datasets by sex and age group. Figure 1: Speech data preprocessing procedures. as the Mel spectrogram, log-Mel spectrogram, MFCCs, and delta MFCCs, were extracted and used for model training. The spectrogram represents the amplitude or intensity of an audio signal at various frequencies over time. It is extracted by dividing the audio signal into short time segments and then performing a Fourier transform on each segment to detect the frequency components within that segment. In the plot, the x-axis represents time and the y-axis represents frequency, with the amplitude of each frequency shown as a heatmap. Lighter colors in the plot indicate higher energy at that frequency, whereas darker colors represent empty or dead sounds. However, because the human auditory system can perceive only a limited range of frequencies and amplitudes, spectrograms do not capture all the information necessary for human-level sound. To mimic the response of the human ear to sound, a Mel filter bank was applied. This represents the amplitude and frequency based on the Mel scale, which corresponds to the frequencies perceived by humans. It is converted from a frequency using Equation (1). \[Mel(f)=2595\,log_{10}\bigg{(}1+\frac{frequency}{700}\bigg{)} \tag{1}\]  
A Mel spectrogram combines the Mel scale with a spectrogram to provide a visual representation in the frequency and time domains. In the plot, the x-axis represents time, and the y-axis represents the Mel-scaled frequency. Instead of showing the amplitude or intensity of the signal, the colors are displayed using a decibel scale. The log-Mel spectrogram is a Mel spectrogram in which a logarithmic transformation has been applied to the Mel-scale frequency, which is the y-axis of the Mel spectrogram [20]. These spectrogram-based features have been used in fields such as speech emotion recognition [21] and environmental sound classification [22]. Figure 2 presents the spectrogram, Mel spectrogram, and log-Mel spectrogram images generated from the audio. MFCCs are a set of characteristic coefficients that represent audio signals based on a Mel filter bank and reflect the properties of the human auditory frequency range. The MFCCs apply a discrete cosine transform (DCT) to the log-Mel spectrum and transform  
Figure 2: Visualization of a spectrogram, Mel spectrogram, and log-Mel spectrogram in audio. it into the time domain, as shown in Equation (2). Here, X_m denotes the log energy of the mth Mel spectrogram, and c denotes the index of the cepstral coefficient. MFCCs have been applied in various fields, including speech emotion recognition [23] and music genre classification [24].