# A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition
## 5 Discussion  
In this section, we further analyzed the effectiveness and robustness of the proposed system over the databases CASIA, EMODB, and SAVEE. We used the common evaluation matrices, such as weighted, unweighted, and F1-score to estimate the class level and the overall accuracy. In order to measure the model prediction performance among the actual  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **WAR** & **UAR** \\ \hline GA-BEL [41] & 38.55 & 38.55 \\ HuWSF [42] & 43.50 & 43.50 \\ RDBN [44] & 48.50 & 48.50 \\ PCRN [9] & 58.25 & 58.25 \\ Bi-LSTM [46] & / & 75.00 \\ Bi-GRU [47] & / & 72.50 \\ CNN [48] & / & 76.67 \\ CLDNN [45] & / & 61.67 \\ CapsNet [49] & / & 63.33 \\ HPCB (Ours) & / & 79.67 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparisons (%) of the model HPCB to those of the peers in the literature on the CASIA database.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **WAR** & **UAR** \\ \hline HuWSF [42] & 81.74 & / \\ RDBN [43] & 82.32 & / \\ LNCMSF [44] & / & 74.46 \\ ACRNN [36] & / & 82.82 \\ PCRN [9] & 86.44 & 84.53 \\ Bi-LSTM [46] & / & 71.03 \\ Bi-GRU [47] & / & 70.09 \\ CNN [48] & / & 78.50 \\ CLDNN [45] & / & 56.07 \\ CapsNet [49] & / & 77.57 \\ HPCB (Ours) & / & 84.65 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparisons (%) of the model HPCB to those of the peers in the literature on the EMODB database.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **WAR** & **UAR** \\ \hline GA-BEL [41] & 44.18 & / \\ HuWSF [42] & 50.00 & / \\ RDBN [43] & 53.60 & / \\ PCRN [9] & 62.49 & 59.40 \\ Bi-LSTM [46] & / & 44.79 \\ Bi-GRU [47] & / & 41.67 \\ CNN [48] & / & 54.17 \\ CLDNN [45] & / & 43.75 \\ CapsNet [49] & / & 56.25 \\ HPCB (Ours) & / & 56.50 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparisons (%) of the model HPCB to those of the peers in the literature on the SAVEE database.  
and the predicted labels, the confusion matrix of each dataset is shown. The confusion among the actual and the predicted labels of each class is shown in certain rows and columns in the confusion matrix. We conducted comprehensive experimentation for three datasets to show the model prediction performance in terms of precision, recall, F1-score, weighted, and unweighted results. We chose an optimal model combination for an efficient SER system.  
The experimental results show that, for SER in different datasets, HPCB achieves higher performance compared with the other methods. The advantage of the CNN network is that it shares convolution kernels and automatically performs feature extraction, making it suitable for the high-dimensional data. However, at the same time, the pooling layer loses a lot of valuable information by ignoring the correlation between the local and the whole. This makes CNN fail to obtain high accuracy in learning time series. When dealing with tasks related to time series, LSTM is usually more appropriate. However, in terms of classification (including SER), LSTM has an obvious disadvantage in performance. Therefore, HPCB gets an obvious advantage in the variable mapping, to preserve the more valuable information. Additionally, it also performs well on the task sensitive to time series. It can be concluded that the proposed model has excellent generalization ability for SER tasks.