## 5 Experiments and Results
### Experiment I: Different Weight Adjustment Methods for MSFL  
To verify the generalization performance of the GradNorm method on MTL, three types of models are used with different weight adjustment methods. The results are shown in Table 5, where the M_MSFL model represents the MSFL model incorporating the Metabalance approach concerning the study [73], the G_MSFL model represents the MSFL model incorporating the GradNorm method concerning the study [67], and the E_MSFL model represents the MSFL model depending on manually adjusting the task weights with experience. The models have the same framework and parameters described \begin{table}
\begin{tabular}{c c c c} \hline
**Purpose** & **Experiment** & **Introduction** & **Result** \\ \hline \multirow{4}{*}{Model Generalization} & \multirow{4}{*}{Experiment I} & \multirow{4}{*}{\begin{tabular}{c} Verify the effect of gradient \\ normalization method introduced \\ in MTL on the generalization \\ ability of the model \\ \end{tabular} } & The gradient normalization method performs best than other methods \\  & & & \\ \cline{3-4}  & & \begin{tabular}{c} Compare the results of \\ generalization ability of MSFL \\ with other baseline models \\ \end{tabular} & \
\begin{tabular}{c} MSFL achieves better results \\ than most models with an \\ average improvement of \\ \end{tabular} \\  & &
\begin{tabular}{c} 3.37\(-\)4.9\% \\ The top 10 MSFs almost \\ contain the top-ranked \\ monolingual features \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 4: The brief conclusion of experiments in our study. \begin{table}
\begin{tabular}{c c c} \hline
**Notation** & **Meaning** & **Value** \\ \hline \(d_{lstm}\) & Number of cells in LSTM & 32 \\ \(d_{att}\) & Number of nodes in attention & 88 \\ \(d_{dml}\) & Number of nodes in FC1 & 512 \\ \(d_{dml2}\) & Number of nodes in FC2 & 300 \\ \(d_{softmax1}\) & Number of nodes in softmax1 & 4 \\ \(d_{softmax2}\) & Number of nodes in softmax2 & 3 \\ \hline \end{tabular}
\end{table} 
Table 3: Settings of hyperparameters in training. in Section 4.3, except for the weight adjustment methods. Specifically, the parameters of relaxation and beta in the M_MSFL model are set to 0.7 and 0.9, respectively, and the alpha parameter in G_MSFL to 0.16. For the weight parameters in E_MSFL, the model for different values of \(w_{1}\) and \(w_{2}\) are evaluated ranging from 0.1 to 0.9, where \(w_{1}\) represents the weight of the emotion recognition task and is named alpha, and \(w_{2}\) represents the weight of the language recognition task and is named beta. The performance for the two tasks of different alpha values is shown in Figure 3. According to Figure 3, assigning different weights to the tasks affects the performance of the model. A too high alpha will result in less assistance in the language recognition task on the emotion recognition task, while a too small alpha will cause the model to be more biased towards learning the language recognition task and ignore the emotion recognition task. By comparing the model performance of different alpha values, the best result of about 80.77% on average was achieved in the E_MSFL model when setting alpha to 0.5 (i.e., 0.5 for the weight of emotion recognition task and 0.5 for the weight of language recognition task). Intuitively, jointly learning representations of emotions with languages helps to uncover the common high-level discriminative representations in multilingual scenarios, which leads to performance improvements in the SER system. As shown in Table 5, all three models achieve about 99% on UAR and ACC in the language recognition task, which means language emotion is an easier task than emotion recognition, and the emotion recognition is challenging. Therefore, the following experimental analysis focuses on the main task of emotion recognition. In terms of the emotion recognition task, the average performance of the G_MSFL model is slightly higher than that of the E_MSFL model and M_MSFL model, with an average improvement of about