# An Ensemble Model for Multi-Level Speech Emotion Recognition
## 4 Design of Experiments and Analysis of Results
### Experiment with Ensemble Learning Model  
From the above experiments, it can be judged that every expert has a positive influence on the accuracy of emotion recognition at their respective angles. For the corpus of sample size balance and data stability, each designed expert has a certain degree of credibility and can complete the emotion recognition task independently. However, after the data are preprocessed, the sample collection often has data skew and irregularities. At this point, the decision of the individual expert may be biased. In the IEMOCAP corpus, the sample size of the existing angry class is low, while that of the happy class is high. Each expert performs differently in this particular situation when making decisions. At this time, according to the ensemble learning scheme, the confidence of each type of emotion of each expert in a special scene can be calculated, thereby improving the recognition accuracy of each type of emotion through the weighting effect. The effect of the model proposed in this paper is measured from the perspective of experimental comparison before and after the ensemble learning and the confidence comparison of several popular models. Experiment 4: Mainly used to test the improvement effect of the model before and after ensemble learning, with a single best-performing expert 3 model as the baseline. Model 1: Three experts + confidence weighting calculations. Model 2: Three experts + confidence calculations (using the first integration scheme). Model 4: Current system, three experts + confidence calculations (using the second integration scheme). Each expert's confidence in each emotion recognition was calculated separately. Table 6 shows the accuracy of speech emotion recognition models after experimental verification. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model/Sample Set** & **WA** & **UA** \\ \hline Baseline: Expert 3 & 70\% & 70\% \\ Model 1: Three experts + & 69\% & 68\% \\ confidence weighting & & \\ Model 2: Three experts + & 73\% & 72\% \\ confidence weighting (using the & & \\ first option) & & \\ Current system: Three experts + & 75\% & 75\% \\ confidence weighting (using the & & \\ second option) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Test results of the ensemble model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model/Sample Set** & **WA** & **UA** \\ \hline Baseline: CNN + BLSTM & 54\% & 53\% \\ Model 1: Level 1 & 61\% & 62\% \\ Model 2: Level 2 & 57\% & 58\% \\ Model 3: CRNN + HSF + BLSTM & 68\% & 68\% \\ Current System: Expert 3, & 72\% & 71\% \\ multi-level & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Test result of expert 3. Experiment 5: Mainly used to compare the effect of the current ensemble learning model and other popular emotion recognition models. Model 1: Emotion LLDs combined with attention mechanism [26]. Model 2: Fully convolutional network combined with attention mechanism [27]. Model 3: DNN on spectrograms [28]. Current system: ensemble model (using the second option). Each expert's confidence in emotion recognition was calculated separately. Table 7 shows the accuracy of speech emotion recognition models after experimental verification. It can be seen from Table 6 that since the ensemble learning model combines the results of multiple experts, every expert fully utilized their own respective advantages. Therefore, the current system has the best WA and UA, exceeding the recognition accuracy of other ensemble learning models. Table 7 lists the results of some common emotion recognition models. By comparison, the WA and UA of the current system are better than those of other models. In addition, through information statistics, it is found that the number of emotions that the third expert guessed wrong and the first two experts answers correct is 446, and the ratio is 0.1; the number of emotions that the third expert answered correctly but the first two experts guessed wrong is 298, and the ratio is 0.06.