## 4 Experiment and Results
### Model Performance
#### 4.4.5 Comparison with Other Research  
The proposed model's performance was compared with existing research. As shown in Table 8, the proposed parallel model demonstrates competitive results, particularly in unweighted accuracy (UA), underscoring its potential in emotion recognition tasks. For instance, the proposed method achieves a UA of 73.72% with Wav2 Vec 2.0 and 70.38% with HuBERT 2.0, showcasing its ability to perform on par with or better than many state-of-the-art models. Compared to models such as the multi-channel model by [PERSON] et al. (2022) [35] (UA: 71.05%) and the BERT-RoBERTa model by [PERSON] et al. (2024) [36] (UA: 74.27%), the proposed method offers a strong balance between weighted accuracy (WA) and UA, validating its effectiveness across diverse benchmarks.  
\begin{table}
\begin{tabular}{c 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 Log Mel-Spectrogram & DCNN & 61.24 & 62.26 & 0.3129 & 0.1516 & 0.4475 & 0.3395 \\ MFCC & 1 DCNN + BiLSTM & 58.09 & 59.14 & 0.2839 & 0.1649 & 0.406 & 0.2807 \\ Short-term Features & 1 DCNN + BiLSTM & 55.42 & 56.6 & 0.3056 & 0.1526 & 0.4397 & 0.3244 \\ Raw PCM & Wav2 Vec 2.0 & 68.84 & 68.91 & 0.3548 & 0.2856 & 0.4549 & 0.3241 \\ Raw PCM & HuBERT & 68.70 & 68.62 & 0.3361 & 0.2694 & 0.4215 & 0.3174 \\ Combined Log & Parallel Model & & & & & & \\ Mel-Spectrogram + MFCC & w/Wav2 Vec 2.0 & 69.28 & 68.71 & 0.3415 & 0.2755 & 0.4308 & 0.3182 \\ + STF + Raw & & & & & & & \\ Combined Log & Parallel Model & & & & & & \\ Mel-Spectrogram + MFCC & & & & & & & \\ + STF + Raw & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance on the joint model.  
\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 & 68.94 & 68.42 & - & - & - \\  & Regression Only & - & - & 0.3817 & 0.3024 & 0.4813 & 0.3616 \\  & Equal Weight & 68.84 & 68.91 & 0.3548 & 0.2856 & 0.4549 & 0.3241 \\  & Task-Specific (Priority on & 67.80 & 67.59 & 0.3379 & 0.2625 & 0.4282 & 0.3230 \\  & Task-Specific (Priority on & & & & & \\  & Regression) & 67.88 & 68.22 & 0.3676 & 0.2904 & 0.4735 & 0.3388 \\  & Dynamic Weighting & 72.66 & 73.72 & 0.3717 & 0.3081 & 0.4684 & 0.3384 \\  & Joint Weighting & 71.03 & 70.38 & 0.3674 & 0.3081 & 0.4555 & 0.3385 \\ \hline \multirow{8}{*}{HuBERT} & Classification Only & 68.73 & 68.42 & - & - & - \\  & Regression Only & - & - & 0.3700 & 0.3000 & 0.4758 & 0.3467 \\ \cline{1-1}  & Equal Weight & 69.49 & 69.45 & 0.3629 & 0.2855 & 0.4682 & 0.3352 \\ \cline{1-1}  & Task-Specific (Priority on & & & & & \\  Classification) & 70.20 & 70.30 & 0.3356 & 0.2623 & 0.4281 & 0.3164 \\ \cline{1-1}  & Task-Specific (Priority on & & & & & \\  Regression) & 69.10 & 69.10 & 0.3727 & 0.2919 & 0.4789 & 0.3475 \\ \cline{1-1}  & Dynamic Weighting & 71.34 & 71.34 & 0.3521 & 0.2794 & 0.4451 & 0.3316 \\ \cline{1-1}  & Joint Weighting & 70.37 & 70.27 & 0.3477 & 0.2846 & 0.4428 & 0.3159 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of weighting schemes with parallel models using Wav2 Vec 2.0 and HuBERT feature encoders.