## 4 Experiments
### Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Angry**} & \multicolumn{2}{c}{**Excited**} & \multicolumn{2}{c}{**Frust**} & \multicolumn{2}{c}{**Happy**} & \multicolumn{2}{c}{**Neutral**} & \multicolumn{2}{c}{**Sad**} & \multicolumn{2}{c}{**Avg**} \\
**Model** & **ac** & **f1** & **ac** & **f1** & **ac** & **f1** & **ac** & **f1** & **ac** & **f1** & **ac** & **f1** & **ac** & **f1** \\ \hline Audio [1] & 82.1 & 46.2 & 55.0 & 30.3 & 45.4 & 42.4 & 12.8 & 17.2 & 48.6 & 41.4 & 77.9 & 48.8 & 53.6 & 37.7 \\ Visual [1] & 80.9 & 53.8 & 84.3 & 56.3 & 68.3 & 53.9 & 90.1 & 43.7 & 75.1 & 55.3 & 86.6 & 55.8 & 80.9 & 53.1 \\ Text [1] & 86.0 & 57.9 & 86.8 & 56.8 & 70.1 & 54.7 & 90.1 & 40.5 & 73.5 & 49.0 & 87.0 & 58.0 & 82.2 & 52.8 \\ LF-LSTM [1] & 71.2 & 49.4 & 79.3 & 57.2 & 68.2 & 51.5 & 67.2 & 37.6 & 66.5 & 47.0 & 78.2 & 54.0 & 71.8 & 49.5 \\ LF-Trans [1] & 81.9 & 50.7 & 85.3 & 57.3 & 60.5 & 49.3 & 85.2 & 37.6 & 72.4 & 49.7 & 87.4 & 57.4 & 78.8 & 50.3 \\ \hline EmoEmbs [10] & 65.9 & 48.9 & 73.5 & 58.3 & 68.5 & 52.0 & 69.6 & 38.3 & 73.6 & 48.7 & 80.8 & 53.0 & 72.0 & 49.8 \\ MulT [14] & 77.9 & 60.7 & 76.9 & 58.0 & 72.4 & 57.0 & 80.0 & 46.8 & 74.9 & 53.7 & 83.5 & 65.4 & 77.6 & 56.9 \\ BIMHA [33] & 77.2 & 57.6 & 78.3 & 56.1 & 73.9 & 54.2 & 83.4 & 43.2 & 76.4 & 50.9 & 83.8 & 63.7 & 78.8 & 54.3 \\ CMHA [16] & 88.6 & 61.1 & 87.9 & 60.5 & 75.1 & 56.3 & 89.0 & 45.8 & 76.5 & 51.2 & 88.3 & 61.6 & 84.3 & 56.1 \\ MESE [1] & 88.2 & 62.8 & 88.3 & 61.2 & 74.9 & 58.4 & 89.5 & 47.3 & 77.0 & 52.0 & 88.6 & 62.2 & 84.4 & 57.4 \\ FE2E [1] & 88.7 & 63.9 & 89.1 & 61.9 & 71.2 & 57.8 & 90.0 & 44.8 & 79.1 & 58.4 & 89.1 & 65.7 & 85.7 & 57.1 \\ Le et al. [12] & **90.1** & 66.8 & 88.5 & 66.8 & 77.7 & 57.0 & **90.5** & 48.5 & 78.1 & 56.6 & 90.7 & 69.6 & 85.9 & 60.9 \\ \hline XMBT\({}_{\text{dra}}\) & 89.5 & **67.8** & **90.8** & **70.7** & **78.9** & **59.9** & 89.9 & **55.5** & **79.1** & **60.6** & **91.4** & **72.9** & **86.6** & **64.6** \\ \(\Delta\) & \(\downarrow\)0.6 & \(\uparrow\)1.0 & \(\uparrow\)1.7 & \(\uparrow\)3.9 & \(\uparrow\)1.2 & \(\uparrow\)1.5 & \(\downarrow\)0.6 & \(\uparrow\)7.0 & – & \(\uparrow\)2.2 & \(\uparrow\)0.7 & \(\uparrow\)3.3 & \(\uparrow\)0.7 & \(\uparrow\)3.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test results on IEMOCAP dataset. insight into how the DRA strategy contributes to the learning rate and overall performance improvement. As depicted in Figure 5, the model trained with the traditional \(\mathcal{L}_{\text{fuse}}\) loss function learned more slowly than models trained with UW losses despite having higher initial metrics at the first epoch. No significant differences occurred in the scores in the training split regarding the UW regularizations. However, the XMBT trained with the DRA loss function (\(\mathcal{L}_{\text{drnw}}\)) learned faster and achieved the highest scores earlier than other UW strategies in the validation and test folds. These scores demonstrated the best performance of the options, indicating the effectiveness of the DRA strategy in accelerating the learning process and achieving superior performance across validation and test datasets. This work compared the CH-SIMS dataset to the proposed method and the baselines presented in [11; 34]. These baselines can employ single- or multilabel annotations. The multilabel approach involves four labels, audio (A), text (T), visual (V), and multimodal (M), which are annotations for a single sample. Generally, the multilabel annotations achieved better results than the single-label counterparts using the same model architecture. Although the baselines use extracted features, the proposed model is trained end-to-end. Consequently, the proposed XMBT trained with DRA loss surpassed the baselines in all evaluation metrics. Table 5 displays the experimental results of the proposed methods and related models on the CH-SIMS dataset.