# A Feature Fusion Model with Data Augmentation for Speech Emotion Recognition
## 5 Experiment Results
### MHA-CRNN and Feature Fusion Model
The size of the attention head reflects the dimensionality of the attention vector obtained by linearly mapping the input vector. A small dimensionality of the mapping may result in the loss of  
Figure 9: Comparison of different attentional head sizes in MHA-CRNN structures. feature information, while too high a dimensionality will make the representation of the original emotional input information sparse and make it difficult for the attention head to focus its attention. 3. To compare the proposed MHA-CRNN model as well as the fusion model, this section uses the above two models with the affective speech data enhancement method Mix-wav and conducts a comparison experiment on the speech emotion benchmark dataset IEMOCAP. Here, the multi-head attention parameters are num heads equal to 8 and head size equal to 512. The fusion input in the fusion model is the 1200-dimensional ISI0_paraling global features after feature selection. Figure 10 shows the test UAA of each of the five-fold cross-validation experiments for both models. 4. After a series of different experimental comparisons and parameter adjustments, we achieved a result of 66.44% on the common four-classification task on the benchmark dataset IEMOCAP, which is better than most research methods using similar models. To compare with more studies in this paper, we report the UAA and WAA methods of the MHA-CRNN in IEMOCAP database and the feature fusion model based on MHA-CRNN, as shown in Table 3.  
This shows the confusion matrix of the best one-time classification results on CHSE-DB and IEMOCAP, as shown in Figure 11. It can be seen that a large amount of data falls on the diagonal of the confusion matrix, i.e., the model classification is mostly correct. The darker the color on the diagonal, the better the task prediction for the category. With the method described in this paper, on the IEMOCAP dataset, the anger class has achieved the highest recognition rate, with a rate of 73.1%. It is difficult to distinguish between neutral and happy emotions, and the probability of mis-classification. One possible reason is the corpus sample problem, and the other is that the model has not yet extracted discriminative emotional features, which needs to be further improved. On the CHSE-DB dataset, the  
Figure 10: Comparison of MHA-CRNN and its feature fusion model in IEMOCAP. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **UAA** & **WAA\%** & **Year** \\ \hline CRNN + Attention [21] & 64.74 & / & 2018 \\ CNN + LSTM [48] & 61.7 & / & 2018 \\ SPP + LSTM + fusion [49] & 64.6 & 64.5 & 2019 \\ Blocked-CRNN [50] & 62.3 & / & 2020 \\ Selective MTL [51] & 59.47 & 56.87 & 2022 \\ MHA-CRNN & 65.57 & 64.45 & 2022 \\ MHA-CRNN + fusion & 66.44 & 65.17 & 2022 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The four categories of tasks on the dataset IEMOCAP are compared with other works. classification performance is significantly better, probably due to the fact that the vocalists of the CHSE-DB dataset are professional performers and the quality of the sample recordings is better than that of IEMOCAP. The performance on both Chinese and English speech emotion datasets proves the effectiveness of the proposed method.