## 5 Discussion
According to [26], color jitter exerts significant effects on helping a model recognize complex or low-quality images. Moreover, since color jitter can increase the brightness of dark images and reduce a degree of saturation in images including excessively saturated colors, it can support the model in extracting features of an image more effectively. In other words, different features of an image can be extracted depending on saturation weights and the degree of color saturation. The comprehensive analysis of experimental results on the ResNet18 and ResNet34 models yielded several key findings. Table 4 indicates that the optimal saturation weights for these models varied when training was conducted using individual saturation weights. Conversely, Table 6 reveals that the optimal saturation weight for these models remained consistent when saturation weights were combined. Furthermore, the performances of these models showed similarities when saturation weights were combined. Accordingly, it was observed that the performances of ResNet18 and ResNet34 increased by up to 23.4% and 24.84%, respectively, when trained on the DB augmented with the DA-S technique with individual saturation weights applied, compared to when training was conducted using Original DB-T with individual saturation weights applied. When model training was performed based on individual saturation weights, the optimal saturation weights for models varied. On the other hand, when model training was performed based on combinations of saturation weights, the optimal saturation weight for models remained the same. Additionally, models exhibited similar performances in this condition. These results suggest that data augmentation with combined saturation weights can facilitate more stable model training compared to using individual saturation weights. Table 7 highlights the improved performance of the SER model proposed in this study and compares it with results obtained from other algorithms. Recent studies have introduced a variety of emotion classification models, image preprocessing methods, and  
Figure 7: Comparative analysis of overfitting that occurred when using Original DB-T and when using the DB augmented with the DA-S technique. (**a**) Mean accuracies calculated based on Original DB-T. (**b**) Mean accuracies calculated based on DAS (0%, =0) and DAS (99.49%, =2). data augmentation techniques, demonstrating their effectiveness using DBs provided on RAVDESS, EMODB, and AI-Hub. However, direct comparisons between these solutions are challenging due to variations in conditions applied, such as methods for SER, utilization of speech data, and types of DBs. Therefore, as indicated in Table 7, this study focused on indirectly comparing model performances by referencing previous studies that utilized the same models or investigated data augmentation methods. [PERSON] et al. [7] conducted speech emotion classification using the same DB and log mel-spectrogram method used in this study. However, since they adopted the LSTM model and applied the root mean square error (RMSE) as an evaluation indicator, the performance of the model presented in their research cannot be directly compared to that of the proposed model in this study. Similarly, multiple studies have explored emotion classification methods based on CNN-based models or LSTM models using the same DBs. [PERSON] et al. [11] present a SER model using an English-based speech DB provided on RAVESS, differing from the DB used in this study. Due to language variations such as accent, loudness, and pitch, direct comparisons between their model and the proposed model in this study are challenging. Moreover, their CNN-based model shows differences in the number of layers and the structure compared to the ResNet-based model of this study, further complicating direct performance comparisons. Notably, [PERSON] et al. [28] achieved a 17.04% higher performance with their ResNet18-based model compared to the 4-layer CNN model by [PERSON] et al. [11], despite the use of the same DB.