## 4 Results
### Performance Evaluation of 2L-DBMM in Fusing SER and SA  
We employed our proposed two-layered dynamic Bayesian mixture model (2L-DBMM) to fuse speech emotion and text sentiment data into a new category of combined emotions, as illustrated in Table 1 with basis on psychological studies. The 2L-DBMM architecture (Section 3.3, Equation (4)) consists of two layers: the first layer comprises two parallel mixture models, one for SER (DBMM1) and one for SA (DBMM2) as presented in Figure 2. Each model integrates the outputs of their respective base classifiers, with weights derived using inverse entropy-based confidence (Equation (2)). This confidence measure, calculated from the training datasets, gauges the uncertainty of each classifier based on its outcomes. During the testing phase, these weights are dynamically updated using Equation (3), ensuring the model adapts to changing classifier performance over time. By analyzing the confidence of each modality (SER and SA), we determine which modality yields more reliable results. The weighted probabilities from each modality are then merged using Equation (4) to derive the final classifications for the combined emotions, as detailed in Tables 15 and 16 for the ESD and EmoUERJ datasets, respectively. * **Performance and Discussion**: The 2L-DBMM demonstrates robustness in merging modalities, assigning higher weights to outputs with lower uncertainty. This approach ensures that the final classification is informed by the most reliable information from both SER and SA. The results showcase the effectiveness of this fusion technique in achieving high accuracy and consistency across both datasets. On the ESD dataset (SER + SA), the 2L-DBMM achieved an average accuracy of 98%, with precision, recall, and F1-score averaging 97%, 98%, and 97%, respectively. Notably, emotions like CE\({}_{9}\) (unsurve/contemplative/ambivalent), CE\({}_{11}\) (furious/enraged/hostile), and CE\({}_{12}\) (an-noyed/iriritated/frustrated) exhibit particularly strong performance, with accuracy and F1-scores consistently exceeding 98%. The 2L-DBMM also performs well on the EmoUERJ dataset (SER + SA), achieving an average accuracy of 96%, with precision, recall, and F1-score averaging 96%, 97%, and 96%, respectively. Positive emotions like CE\({}_{4}\) (joyful/elated/enthusiastic) and CE\({}_{6}\) (content/satisfied/peaceful) and negative emotions like C\({}_{11}\) (furious/enraged/hostile) and C\({}_{12}\) (annoyed/iriritated/frustrated) show exceptional performance, with accuracy and F1-scores of 98%. While other negative emotions like CE\({}_{2}\) (depressed/hopeless/despairing) and CE\({}_{3}\) (disappointed/melacholic/appathetic) have slightly lower metrics, the overall results remain strong, over 92%. These results are particularly significant as they represent the first attempt to combine the ESD and EmoUERJ datasets for multimodal fusion of speech emotion and text sentiment. The superior performance of the 2L-DBMM highlights the potential of this approach in advancing affective communication analysis and suggests promising applications in various domains, including mental health assessment, human-computer interaction, and cross-cultural communication. The 2L-DBMM's ability to dynamically adapt to the strengths and weaknesses of different classifiers and modalities is crucial for achieving this high level of performance. Further analysis of the 2L-DBMM's inner workings reveals that the model tends to assign higher weights to the SER modality when dealing with emotions that are strongly expressed vocally, such as anger and happiness. Conversely, for emotions that are more subtly conveyed or that heavily rely on context, such as neutral or ambivalent states, the model gives more weight to the SA modality. This adaptive weighting scheme al