## 5 Discussion
The model developed by [PERSON] et al. [28] shares similar aspects with the proposed model in this study, confirming the effectiveness of ResNet18. [PERSON] et al. [27] present a model using a German-based speech DB provided on EMODB, which differs from the DB employed in this study. Additionally, their 3-layer 2D CNN model exhibits differences in the number of layers and the structure compared to the ResNet-based model proposed in this study. Consequently, direct performance comparisons between these models are not feasible. However, [PERSON] et al. [27] \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Publication** & **Database** & \begin{tabular}{c} **Processing and** \\ **Features** \\ \end{tabular} & \begin{tabular}{c} **Data** \\ **Augmentation** \\ \end{tabular} & \
\begin{tabular}{c} **Classifier** \\ **Class** \\ \end{tabular} &
\begin{tabular}{c} **No. of** \\ **Class** \\ \end{tabular} & **Effective** \\ \hline \multirow{3}{*}{Present study} & \multirow{3}{*}{AlHub DB} & Log & - & ResNet18 & 4 & Test Acc. 56.96\% \\  & & Mel-Spectrogram & & & & \\  & & Log & & & & \\ \multirow{2}{*}{Present study} & \multirow{2}{*}{AlHub DB} & Mel-Spectrogram & Saturation & ResNet18 & 4 & Test Acc. 91.15\% \\  & & & Log & & & & \\ \multirow{2}{*}{Present study} & \multirow{2}{*}{AlHub DB} & Mel-Spectrogram & - & ResNet18 & 7 & Test Acc. 50.54\% \\  & & Mel-Spectrogram & & & & \\ \multirow{2}{*}{[PERSON] et al. [7]} & \multirow{2}{*}{AlHub DB} & Log & Saturation & ResNet18 & 7 & Test Acc. 90.47\% \\  & & Log & & & & \\ \multirow{2}{*}{[PERSON] et al. [11]} & \multirow{2}{*}{RAVDESS} & Mel-Spectrogram & - & LSTM & 5 & RMSE. 0.2174 \\  & & Log & & & 2D & \\ \multirow{2}{*}{[PERSON] et al. [27]} & \multirow{2}{*}{EMODB} & Mel-Spectrogram & - & CNN(4 layer) & 7 & Test Acc. 66\% \\  & & Mel-Spectrogram & & & & \\ \multirow{2}{*}{\({}^{\star}\)[PERSON] et al. [27]} & \multirow{2}{*}{EMODB} & Log & - & CNN(3 layer) & 7 & Test Acc. 74\% \\  & & Mel-Spectrogram & & & & \\ \multirow{2}{*}{[PERSON] et al. [28]} & \multirow{2}{*}{RAVDESS} & Log & - & ResNet18 & 8 & Test Acc. 83.07\% \\ \hline \hline \end{tabular}  
* The symbol \({}^{\star}\) indicates that data augmentation has been applied. Here, AlHub DB refers to a conversational speech dataset for emotion classification among speech DBs provided by AI-Hub, an AI integration platform operated by the National Information Society Agency.  
\end{table} 
Table 7: Comparison of emotion recognition methods using speech databases. reported a 14% increase in performance when using an augmented database compared to the original database. This trend is similar to the findings observed with the proposed model in this study. In particular, the proposed model demonstrated an improvement from 50.54% to 90.47% in the seven-class experiment, suggesting that data augmentation and domain adaptation strategies may contribute to performance enhancement. Nevertheless, due to the differences in databases used in this study and [PERSON] et al. [27], direct comparisons remain challenging. These findings further demonstrate that not only increasing the size of the dataset but also applying the DA-S technique significantly enhances emotion classification performance. Additionally, when comparing the performance of different saturation parameters, the combination of weights 0 and 2 yielded the highest accuracy in emotion classification. These findings emphasize the potential of the DA-S technique in addressing the limitations of existing SER systems and improving their robustness.