## 5 Experimental Results
### Results on Speaker Models--TL from Speaker Recognition Task  
This section examines the impact of pre-trained models for speaker recognition (or more specifically for the verification task), including the d-vector, x-vector, and r-vector systems, along with our enhanced model versions, on the performance of the downstream SER task. Initially, these models were also trained from scratch using emotional datasets only. Improvements in SER accuracy were examined after applying TL from the speaker recognition domain. Based on the results (see Table 9), the following conclusions can be drawn:  
* When the baseline models were sorted according to accuracy, d-vector had the lowest accuracy, followed by x-vector, and then r-vector. Parallels can be drawn between the performance of the d-vector on the SER task and its performance in speaker recognition, as d-vector was generally outperformed by more advanced techniques like x-vectors. In contrast, the proposed modification to r-vector outperformed other models, across all settings, making it the top performer. * TL notably improved the performance of the baseline x-/ r-vector models across both datasets. For x-vector (baseline) using IEMOCAP, UA and WA increased by 12.1% and 12.3%, respectively. In contrast, using CREMA-D, the improvements were less pronounced, with a 7.5% increase in UA and a 7.8% increase in WA. For r-vector (baseline) using IEMOCAP, UA and WA increased by 11.8% and 12.1%, respectively. For CREMA-D, both metrics showed an increase of approximately 10%. * The enhanced versions of the x-/r-vector systems consistently outperformed the baseline models on both datasets, both before and after applying TL, indicating that the proposed modifications were broadly beneficial. Specifically, using TL, the enhanced r-vector achieved 72.89% UA and 73.41% WA on IEMOCAP, and 79.34% UA and 79.72% WA on CREMA-D, suggesting that it is highly effective in capturing the nuances of speech emotion. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**IEMOCAP**} & \multicolumn{6}{c}{**CREMA-D**} \\ \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Features**} & \multicolumn{2}{c}{**Without TL**} & \multicolumn{2}{c}{**With TL**} & \multicolumn{2}{c}{**Without TL**} & \multicolumn{2}{c}{**With TL**} \\ \cline{3-10}  & & **UA [\%]** & **WA [\%]** & **UA [\%]** & **WA [\%]** & **UA [\%]** & **WA [\%]** & **UA [\%]** & **WA [\%]** \\ \hline \multirow{3}{*}{GoogLeNet} & Scalogram (Morse) & 43.81 & 43.57 & 54.14 & 56.57 & 53.62 & 54.37 & 63.74 & 64.87 \\  & Scalogram (Morlet) & 46.15 & 44.52 & 55.22 & 59.21 & 54.15 & 56.52 & 65.22 & 66.21 \\  & 3D Mel spectrogram & 43.32 & 41.64 & 54.55 & 54.55 & 52.32 & 53.64 & 63.55 & 64.54 \\ \hline \multirow{3}{*}{AlexNet} & Scalogram (Morse) & 46.45 & 44.34 & 52.32 & 55.06 & 53.85 & 54.82 & 62.32 & 65.06 \\  & Scalogram (Morlet) & 47.08 & 45.24 & 55.71 & 57.63 & 55.48 & 57.04 & 65.71 & 67.63 \\  & 3D Mel spectrogram & 45.82 & 43.65 & 52.40 & 53.59 & 53.37 & 53.65 & 62.40 & 63.59 \\ \hline \multirow{3}{*}{SqueezeNet} & Scalogram (Morse) & 46.59 & 44.48 & 52.43 & 55.24 & 56.59 & 57.48 & 62.43 & 65.24 \\  & Scalogram (Morlet) & 47.34 & 45.36 & 55.82 & 57.79 & 57.34 & 58.76 & 65.82 & 67.79 \\  & 3D Mel spectrogram & 45.90 & 43.71 & 52.61 & 53.68 & 55.90 & 56.71 & 62.61 & 63.68 \\ \hline \multirow{3}{*}{ResNet-18} & Scalogram (Morse) & 51.08 & 52.38 & 61.74 & 60.87 & 62.08 & 62.38 & 69.74 & 69.87 \\  & Scalogram (Morlet) & 53.64 & 53.23 & 62.48 & 61.13 & 63.64 & 63.83 & 70.18 & 70.45 \\  & 3D Mel spectrogram & 52.54 & 51.34 & 60.52 & 59.44 & 61.54 & 61.36 & 68.22 & 68.44 \\ \hline \multirow{3}{*}{ResNet-50} & Scalogram (Morse) & 53.91 & 53.25 & 62.69 & 62.54 & 63.91 & 63.25 & 69.69 & 70.54 \\  & Scalogram (Morlet) & 54.37 & 54.12 & 64.75 & 63.13 & 64.37 & 64.52 & 70.75 & 71.13 \\ \cline{1-1}  & 3D Mel spectrogram & 52.41 & 51.95 & 61.98 & 60.46 & 62.41 & 62.95 & 69.38 & 69.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of SER performance using image classification models with and without TL.