## 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}{**Disgust**} & \multicolumn{2}{c}{**Fear**} & \multicolumn{2}{c}{**Happy**} & \multicolumn{2}{c}{**Sad**} & \multicolumn{2}{c}{**Surprise**} & \multicolumn{2}{c}{**Avg**} \\
**Model** & **wa** & **f1** & **wa** & **f1** & **wa** & **f1** & **wa** & **f1** & **wa** & **f1** & **wa** & **f1** & **wa** & **f1** \\ \hline Audio [1] & 53.9 & 40.5 & 61.0 & 35.7 & 59.0 & 19.6 & 50.0 & 69.3 & 61.2 & 45.8 & 58.4 & 21.7 & 57.2 & 38.8 \\ Visual [1] & 58.9 & 38.2 & 63.2 & 37.6 & 59.1 & 21.8 & 55.7 & 70.3 & 56.2 & 42.8 & 53.0 & 17.9 & 57.7 & 38.1 \\ Text [1] & 65.9 & 48.4 & 74.0 & 56.0 & 62.8 & 27.0 & 62.3 & 72.0 & 60.2 & 45.3 & 60.9 & 26.0 & 64.3 & 45.8 \\ LF-LSTM [1] & 64.5 & 47.1 & 70.5 & 49.8 & 61.7 & 22.2 & 61.3 & 73.2 & 63.4 & 47.2 & 57.1 & 20.6 & 63.1 & 43.3 \\ LF-Trans [1] & 65.3 & 47.7 & 74.4 & 51.9 & 62.1 & 24.0 & 60.6 & 72.9 & 60.1 & 45.5 & 62.1 & 24.2 & 64.1 & 44.4 \\ \hline EmoEmbs [10] & 66.8 & 49.4 & 69.6 & 48.7 & 63.8 & 23.4 & 61.2 & 71.9 & 60.5 & 47.5 & 63.3 & 24.0 & 64.2 & 44.2 \\ MulT [14] & 64.9 & 47.5 & 71.6 & 49.3 & 62.9 & 25.3 & 67.2 & 75.4 & 64.0 & 48.3 & 61.4 & 25.6 & 65.4 & 45.2 \\ BIMHA [33] & 65.3 & 47.4 & 70.5 & 48.9 & 61.8 & 24.7 & 65.8 & 72.1 & 62.6 & 47.9 & 62.5 & 24.9 & 64.8 & 44.3 \\ CMHA [16] & 65.9 & 49.1 & 73.6 & 53.2 & 63.4 & 27.3 & 65.2 & 72.1 & 64.2 & 46.7 & 64.5 & 26.6 & 66.1 & 45.8 \\ MESE [1] & 66.8 & 49.3 & 75.6 & 56.4 & 65.8 & 28.9 & 64.1 & 72.3 & 63.0 & 46.6 & 65.7 & 27.2 & 66.8 & 46.8 \\ FE2E [1] & 66.9 & 49.5 & 75.4 & 57.2 & 63.8 & 27.1 & 61.9 & 72.3 & **65.6** & 49.3 & 61.5 & 26.9 & 65.8 & 47.0 \\ [PERSON] et al. [12] & 67.5 & 50.2 & 76.3 & 57.0 & 69.0 & 29.0 & 63.0 & 72.6 & 65.5 & 49.2 & 65.7 & 27.6 & 67.8 & 47.6 \\ \hline XMBT\({}_{\text{dra}}\) & **71.4** & **52.6** & **81.4** & **57.4** & **80.5** & **30.2** & **68.5** & **75.4** & 63.9 & **51.6** & **80.3** & **30.3** & **74.3** & **49.6** \\ \(\Delta\) & \(\uparrow\)3.9 & \(\uparrow\)2.4 & \(\uparrow\)5.1 & \(\uparrow\)0.2 & \(\uparrow\)11.5 & \(\uparrow\)1.2 & \(\uparrow\)1.3 & – & \(\downarrow\)1.7 & \(\uparrow\)2.3 & \(\uparrow\)14.6 & \(\uparrow\)2.7 & \(\uparrow\)6.5 & \(\uparrow\)2.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Test results on CMU-MOSEI dataset. Figure 4: CMU-MOSEI and IEMOCAP test results for XMBT by training strategy. This work implemented the open-ended version of the MBT, which has a central modality among \(m\) modalities. The remaining \((m-1)\) modalities transfer information to the central modality using \((m-1)\) corresponding bottleneck tokens. Figure 6 illustrates the experimental results with the open-ended MBT (OE-MBT) and XMBT, considering the number of bottleneck layers, ranging from one to two. In this context, OE-MBT-A, OE-MBT-T, and OE-MBT-V denote the open-ended MBT model with audio-, text-, and visual-central modalities, respectively. Therefore, XMBT achieved the best average accuracy and F1 score compared to the open-ended paradigms. Moreover, XMBT does not require external knowledge to select a central modality, and every modality equally contributes to the final prediction. This study introduced two new hyperparameters associated with two contributions: the number of bottleneck layers (\(L\)) in XMBT and the temperature (\(T\)) for the _softmax_ function in the DRA loss function. The selection of appropriate values for these hyperparameters is crucial because they can significantly influence model performance. A temperature that is too high may cause the model to focus on a specific modality representation while ignoring others. Conversely, a temperature that is too low may cause the loss terms to have equal weights, leading to a degenerate case similar to that in mean average weighting. There  
Figure 5: Evaluation metrics of IEMOCAP.