## 5 Experimental Results
### Results on Augmented Data  
This section examines the impact of data augmentation, specifically the addition of reverberation to emotional utterances, on the performance of both image-based and speaker-based models for SER. It focuses on how the introduction of augmented data affects accuracy, especially when combined with TL. Table 10 presents the accuracy results for the selected models: ResNet-18, ResNet-50, and enhanced versions of the x-/r-vector architectures. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**IEMOCAP**} & \multicolumn{4}{c}{**CREMA-D**} \\ \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Without TL**} & \multicolumn{2}{c}{**With TL**} & \multicolumn{2}{c}{**Without TL**} & \multicolumn{2}{c}{**With TL**} \\ \cline{2-10}  & **UA [\%]** & **WA [\%]** & **UA [\%]** & **WA [\%]** & **UA [\%]** & **WA [\%]** & **UA [\%]** & **WA [\%]** \\ \hline ResNet-18 & 53.64 & 53.23 & 62.48 & 61.13 & 63.64 & 63.83 & 70.18 & 70.45 \\ \hline ResNet-18 & 53.89 & 54.05 & 61.74 & 62.95 & 64.16 & 64.37 & 72.22 & 72.39 \\ + augmentation & & & & & & & & \\ \hline ResNet-50 & 54.37 & 54.12 & 64.75 & 63.13 & 64.37 & 64.52 & 70.75 & 71.13 \\ \hline ResNet-50 & 54.77 & 54.91 & 63.86 & 65.32 & 65.08 & 65.74 & 72.57 & 72.68 \\ \hline Enhanced x-vector & 58.62 & 58.95 & 70.68 & 71.23 & 68.32 & 68.68 & 78.46 & 78.94 \\ \hline Enhanced x-vector & 59.24 & 59.63 & 71.12 & 71.84 & 68.75 & 68.98 & 78.86 & 79.33 \\ + augmentation & & & & & & & & \\ \hline Enhanced r-vector & 60.08 & 60.36 & 72.89 & 73.41 & 68.96 & 69.13 & 79.34 & 79.72 \\ \hline Enhanced r-vector & 61.13 & 61.57 & 73.68 & 74.05 & 69.34 & 69.86 & 79.81 & 80.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Impact of data augmentation on emotion recognition performance with and without TL. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**IEMOCAP**} & \multicolumn{4}{c}{**CREMA-D**} \\ \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Without TL**} & \multicolumn{2}{c}{**With TL**} & \multicolumn{2}{c}{**Without TL**} & \multicolumn{2}{c}{**With TL**} \\ \cline{2-9}  & **UA [\%]** & **WA [\%]** & **UA [\%]** & **WA [\%]** & **UA [\%]** & **WA [\%]** & **UA [\%]** & **WA [\%]** \\ \hline d-vector & 51.36 & 52.06 & 63.18 & 63.92 & 61.84 & 62.18 & 70.25 & 70.16 \\ \hline x-vector (baseline) & 57.37 & 58.14 & 69.49 & 70.44 & 66.85 & 67.08 & 74.36 & 74.87 \\ \hline Enhanced x-vector (proposed) & 58.62 & 58.95 & 70.68 & 71.23 & 68.32 & 68.68 & 78.46 & 78.94 \\ \hline r-vector (baseline) & 58.47 & 58.91 & 70.24 & 71.02 & 67.71 & 68.04 & 77.90 & 78.06 \\ \hline Enhanced r-vector (proposed) & 60.08 & 60.36 & 72.89 & 73.41 & 68.96 & 69.13 & 79.34 & 79.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of SER performance using speaker embedding models with/without TL [63]. The accuracy (both UA and WA) of the ResNet image models is lower compared to the speaker-based models. When augmentation was applied to image-based models, the performance showed a slight improvement in most cases. For instance, for ResNet-18 using IEMOCAP, WA increased from 53.23% to 54.05% without TL. The biggest gains were observed when augmentation was combined with TL. For CREMA-D, ResNet-50 with augmentation achieved a significant boost (from 70.18% to 72.22% UA). However, after applying TL, augmentation caused a unique decrease in accuracy for both ResNet models using IEMOCAP, with a drop of 0.74% and 0.89% in UA. Enhanced \(\times\)-/r-vector models perform considerably better than the ResNet models. For example, the enhanced r-vector model with augmentation reaches a UA of 68.75% on IEMOCAP without TL, compared to ResNet-50's 65.08%. The introduction of augmentation for speaker-based models yielded an increase in accuracy across all settings and both datasets, with an average increase equal to 0.6%. The enhanced r-vector with augmentation and TL is the best-performing combination, achieving the highest UA (79.81%) and WA (80.25%) across both datasets.