## 5 Experimental Results
### Results on Image Models--TL from Computer Vision Domain  
This section examines the effectiveness of the pre-trained image classification models when applied to the task of SER. Specifically, the models considered include GoogLeNet, AlexNet, SqueezeNet, ResNet-18, and ResNet-50, all of which were fine-tuned for SER using scalogram and spectrogram representations of downstream speech data. Table 8 presents the obtained results for the IEMOCAP and CREMA-D datasets, comparing the accuracy of models with and without the use of TL. According to the results, ResNet-50 consistently outperformed the other image recognition models, both with and without TL, making it the top performer across both datasets. Detailed conclusions can be drawn as follows:  
* When comparing the different types of input features, scalogram (Morlet wavelet) consistently achieved the highest accuracy across all models and datasets. This feature type, which closely relates to human auditory perception, proved particularly effective in conjunction with ResNet-50, where it contributed to the highest UA and WA scores on both datasets. In contrast, the 3D Mel spectrogram generally resulted in lower accuracy compared to scalogram features.
* By applying TL, ResNet-50 achieved the highest accuracy on IEMOCAP, with a UA of 64.75% and a WA of 63.13%, showing improvements of up to 10% and 9%, respectively, compared to the results without TL. Similarly, on CREMA-D, ResNet-50 achieved a UA of 70.75% and a WA of 71.13%, with both metrics improving by slightly more than 6%. These results highlight ResNet-50's superior ability to adapt to SER when fine-tuned from image classification tasks, making it the most effective model in this comparison.
* ResNet-18 also demonstrated strong performance with TL, particularly on CREMA-D, achieving a UA of 70.18% and a WA of 70.45%, with both metrics improving by approximately 6.5%. While ResNet-18 does not achieve the same level of accuracy as ResNet-50, it offers a more lightweight architecture, making it suitable for applications with lower computational demands.
* It is worth noting that models such as AlexNet, GoogLeNet and SqueezeNet, when trained from scratch on the IEMOCAP dataset, have shown accuracies below 50%. For a multi-class problem with more than two classes, a 50% accuracy might be better than random guessing but still is not satisfactory. In the case of an imbalanced dataset, low accuracy might also indicate that the model is not effectively learning the minority class.
* Despite their low accuracy, GoogLeNet and AlexNet showed the highest improvements after the application of TL. For the IEMOCAP dataset, GoogLeNet achieved an improvement in UA of 9% and a WA of 14.7%, while AlexNet saw improvements of 8.6% in UA and 12.4% in WA. For the CREMA-D dataset, both models showed better accuracy with TL, but they still lagged behind the ResNet models, with GoogLeNet achieving a UA of 65.22% and a WA of 66.21%, and AlexNet reaching a UA of 65.71% and a WA of 67.63%.
* After applying TL, SqueezeNet is on par with AlexNet in terms of accuracy (a UA of 55.82% and a WA of 57.79%) for IEMOCAP, showing a very similar improvement range. For CREMA-D, SqueezeNet outperformed AlexNet, but it did not achieve the TL-induced improvement as prominently. SqueezeNet's smaller model size and reduced complexity, compared to AlexNet, did not negatively impact the accuracy.
* Experiments on IEMOCAP showed a more prominent improvement in accuracy following the application of TL. Further experiments should be conducted to confirm high efficiency of TL in mitigating the impact of imbalanced datasets.