## 6 Conclusions  
This study introduces a ResNet-based model that improves SER performance by using the log mel-spectrogram to extract speech features and by applying the DA-S technique to perform data augmentation. Additionally, it analyzes the performance of the proposed model using a DB provided on AI-Hub, comparing results augmented with the DA-S technique with those not augmented. Additionally, it analyzed the optimal saturation weights for ResNet models (18-layer and 34-layer) under different conditions, including scenarios with individual saturation weights or combinations of saturation weights. Analytic results of this study indicated that models exhibited higher performance when the DB size was doubled with the DA-S technique applied compared to when the DB size was doubled without the DA-S technique. Regardless of the use of the DA-S technique, the performance of models was enhanced when the dataset size increased. In experiments where the same number of data samples was applied, the performance of models applying the DA-S technique was higher, by from 8.42% to 24.9%, than that of models not applying the DA-S technique. Moreover, when individual saturation weights were applied, the optimal saturation weights for ResNet18 and ResNet34, which led to their highest performance, differed. On the other hand, when combinations of saturation weights were applied, the optimal saturation weights for these models remained consistent. Based on the comparison results, this study presented a ResNet18-based model applying the DA-S technique and the combination of saturation weights 0 and 2, which yielded the best outcomes among the combinations of saturation weights. Through experiments, this study verified that SER models showed higher performance by the increased dataset size. It also confirmed that their performances were enhanced by data augmentation by applying the DA-S technique. The results of this study suggest that the DA-S technique can be applied to solve the overfitting problem that may occur due to the lack of Korean speech DBs. In the future, follow-up experiments using various data augmentation techniques will be conducted. Despite these promising findings, this study acknowledges certain limitations and outlines four potential directions for improvement. Firstly, its performance may vary when a different DB is employed. These changes in performance depending on DBs may arise due to variations in the mean saturation of the entire images included in each DB caused by differences in the saturation of the images. Such variations in saturation levels can significantly impact model performance, making it challenging to guarantee excellent performance when using individual saturation weights. Furthermore, this study found that the proposed model exhibited higher performance when combinations of saturation weights were applied to when individual saturation weights were applied. However, trial and error is required to find the optimal saturation weights suitable for each model and DB. Furthermore, additional research on generalizing the DA-S technique will be conducted to develop methods that mitigate the impact of saturation variations across different DBs, ensuring consistent model performance regardless of database differences. Secondly, this study employed only a single Korean speech DB and focused primarily on ResNet-based models. Although the proposed method showed superior performance compared to traditional ResNet models, the experiments were limited to ResNet architectures. To overcome this limitation, future research will explore state-of-the-art deep learning architectures, such as Transformer models, to evaluate their compatibility with the proposed technique. Additionally, the integration of multimodal datasets, including audio and visual inputs, will be investigated to further validate the practicality and scalability of the proposed method across diverse input types and environments. These efforts aim to solidify the expansion and practical applicability of the DA-S technique in real-world scenarios.