## 6 Discussion  
In this study, we explore various methodologies to improve the performance of SER systems, focusing on the effects of data augmentation and TL using models from two different source domains: image classification (computer vision) and speaker recognition. The comparison of these approaches has provided valuable insights into their respective strengths and weaknesses. The TL technique, when applied independently, has a significant positive impact on the performance of SER using both pre-trained image classification and speaker recognition models. When evaluating the overall impact of transferring knowledge from image-based models, an average increase of 9.1% in UA and 11.3% in WA was observed across models using the IEMOCAP dataset. For CREMA-D, both metrics showed an average increase of 8.5%. When assessing the impact of TL from speaker-based models, the improvements were higher, with an approximately 12% increase in both metrics for IEMOCAP and a 9% increase for CREMA-D. The results demonstrate that TL is effective for enhancing SER performance, especially when leveraging speaker-based models. Data augmentation through the application of reverberation had a subtle but positive effect across all models. An exception was observed for the image-based models using IEMOCAP, where accuracy decreased when TL was applied in conjunction with augmentation. This raises the question of whether the imbalanced nature of the IEMOCAP dataset could lead to this instability in SER accuracy. Figure 8 presents a comparison of model accuracy in the SER tasks using two best-performing models from both the image recognition domain (ResNet-18, ResNet-50) and speaker recognition domain (enhanced versions of \(\times\)-/r-vector architectures). The performance metric used is UA to better understand how well the model is performing on under-represented classes, ensuring a balanced evaluation. A high UA score indicates that the model is performing well across all classes, not just the most frequent ones. The figure highlights individual increments in accuracy caused by the augmentation technique and TL. Following our previous work [17], the proposed enhancements of speaker-based models were also included. In Figure 8, the baseline performance (green) represents the starting accuracy of each model without any enhancement, augmentation, or TL. The speaker-based models start with a higher performing baseline compared to image-based models. The enhancement (blue) refers to the proposed modifications applied to the speaker-based models, which lead to noticeable improvements for the x-/r-vector. Augmentation (red) introduces additional slight improvements for models. TL (grey) shows the largest increase in accuracy, especially for the x-/r-vector models, where TL significantly boosts their performance. Among the image-based models, ResNet-50, when used with TL and paired with scalogram input features, demonstrated the best accuracy. However, despite their competitive results, the overall performance of image-based models is lower compared to speaker recognition models. This difference is likely due to the domain similarity between speaker recognition and SER, which offers an advantage in capturing and classifying emotional nuances in speech. The combination of all techniques (enhancements, augmentation, and TL) resulted in highest accuracy. On IEMOCAP, the best-performing model is the r-vector with a UA of 73.68%, followed closely by x-vector (71.12%). On the CREMA-D dataset, x-vector achieves a UA of 78.86%, and r-vector reaches 79.81%, indicating higher performance on this dataset compared to IEMOCAP. The field of SER has seen significant advancements, with numerous systems being evaluated using the IEMOCAP dataset as a standard benchmark. To ensure our results are comparable with existing research, we employed the Leave-One-Session-Out (LOSO) 5-fold cross-validation method, where four sessions were used for training and the remaining session for testing. This is a common approach for evaluating models on the IEMOCAP dataset.