## 4 Experiment and Results
### Model Performance
#### 4.4.4 Performance with Parallel Models  
The parallel model architecture, combining multiple feature encoders, was evaluated using different weighting schemes. The results, as detailed in Table 6, demonstrate the advantages of the joint model across individual features such as MFCC, Short-term Features (STF), and the Log Mel-spectrogram. The joint model achieves higher WA, UA, and CCC values when combining these features with raw PCM inputs. For instance, the parallel model with HuBERT achieved a WA of 69.49% and a UA of 69.45%, with improvements observed in valence (V), arousal (A), and dominance (D) dimensions. Furthermore, Table 5 highlights the impact of weighting schemes on both classification and regression tasks when using Wav2 Vec 2.0 and HuBERT feature encoders. The dynamic weighting scheme for Wav2 Vec 2.0 achieved a WA of 69.31%, a UA of 68.37%, and a mean CCC of 0.3736, while joint weighting with HuBERT yielded the highest WA and UA scores of 70.37% and 70.27%, respectively. These results demonstrate the importance of optimizing weighting schemes to maximize the model's performance across all emotion dimensions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Loss Function Combination** & \
\begin{tabular}{c} **Weighted** \\ **Average** \\ **(WA)** \\ \end{tabular} &
\begin{tabular}{c} **Unweighted** \\ **Average** \\ **(UW)** \\ \end{tabular} \\ \hline Cross Entropy + CCC Loss & 72.66 & 73.72 \\ Cross Entropy + MAE Loss & 69.37 & 69.47 \\ KL Divergence + CCC Loss & 67.72 & 68.10 \\ KL Divergence + MAE Loss & 66.96 & 66.15 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of different loss function combinations on the joint model with parallel feature encoders (Wav2 Vec 2.0).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Features** &
\begin{tabular}{c} **Feature** \\ **Encoder** \\ \end{tabular} & **WA** & **UA** & **Mean CCC** & **V** & **A** & **D** \\ \hline \multirow{8}{*}{Wav2 Vec 2.0} & Classification Only & 68.96 & 68.15 & - & - & - & - \\  & Regression Only & - & - & 0.3673 & 0.3004 & 0.4585 & 0.3431 \\  & Equal Weight & 69.28 & 68.71 & 0.3415 & 0.2755 & 0.4308 & 0.3182 \\  & Task-Specific (Priority on & 68.81 & 68.62 & 0.3185 & 0.2590 & 0.4028 & 0.2938 \\  & Task-Specific (Priority on & 66.18 & 66.67 & 0.3500 & 0.2841 & 0.4365 & 0.3295 \\  & Dynamic Weighting & 69.31 & 68.37 & 0.3736 & 0.2841 & 0.4798 & 0.3570 \\  & Joint Weighting & 69.10 & 69.21 & 0.3422 & 0.2679 & 0.4351 & 0.3237 \\ \hline \multirow{8}{*}{HuBERT} & Classification Only & 68.37 & 68.19 & - & - & - & - \\  & Regression Only & - & - & 0.3434 & 0.3223 & 0.4806 & 0.2273 \\ \cline{1-1}  & Equal Weight & 68.70 & 68.62 & 0.3361 & 0.2694 & 0.4215 & 0.3174 \\ \cline{1-1}  & Task-Specific (Priority on & 68.96 & 68.52 & 0.2655 & 0.2396 & 0.3295 & 0.2273 \\ \cline{1-1}  & Task-Specific (Priority on & 67.41 & 68.03 & 0.3573 & 0.2789 & 0.4568 & 0.3361 \\ \cline{1-1}  & Dynamic Weighting & 69.31 & 69.35 & 0.3368 & 0.2705 & 0.4288 & 0.3112 \\ \cline{1-1}  & Joint Weighting & 70.37 & 70.27 & 0.3477 & 0.2846 & 0.4428 & 0.3159 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of weighting schemes with Wav2 Vec 2.0 and HuBERT feature encoders.  
The results in Table 7 underline the robustness of the parallel model architecture when paired with adaptive weighting strategies. By effectively leveraging features from Wav2 Vec 2.0 and HuBERT encoders and optimizing task importance dynamically, the model demonstrates its capacity to outperform classification-only or regression-only models, achieving a strong balance between accuracy and interpretability across both discrete and dimensional emotion representations.