# Speech Sentiment Analysis Using Hierarchical Conformer Networks
## 5 Results and Analysis  
The experimental results of our suggested model framework on two datasets are shown in Figure 6, our introduced method achieves 80% (Accuracy) and 81% (Accuracy) on datasets IEMOCAP and RAVDESS, respectively. Table 3 presents the experimental results of various benchmark models on the dataset IEMOCAP, including evaluation metrics such as weighted, unweighted, etc., to show the strength of the proposed method. From the experimental point of view, our proposed method produces relatively good results. In particular, our method is significantly more accurate than [17]. This shows that the transformer structure is more suitable than the LSTM model for processing speech sentiment analysis tasks, and the attention-based GRUs are also better than the stacked GRUs for processing features. Comparing with method [15], it can be demonstrated that the combination of convolution and transformer can significantly enhance the extraction of feature information and can reduce its time complexity. In addition, we also compared the methods with and without attention in the model. The experimental results show that the attention mechanism plays a very important role in the deep learning model. To demonstrate the strength of our model, we also classify different emotions on the dataset IEMOCAP, as shown in Figure 7. According to the experimental results, it can be seen that our model is not as effective for the \"happy\" emotion as other types of emotion. In this regard, by comparing the effects of other methods and models, we conclude that a possible reason is that such emotions are underrepresented in the dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Year** & **WA (\%)** & **UA (\%)** & **Acc. (\%)** \\ \hline CNN+BiLSTM & 2020 & 74 & 72 & 72.5 \\ BiLSTM+DBN & 2021 & - & - & 72.3 \\ DCNN & 2020 & - & - & 64.3 \\ DSCNN & 2019 & 76 & 72 & 73.8 \\ HA-ACNN & 2021 & 76.2 & 76.4 & - \\ ConvLSTM+GRUs & 2020 & 77 & 75 & 78 \\ Conformer+AUGRUs (ours) & 2022 & 80 & 78 & 80 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy summary obtained by various models using the IEMOCAP database. WA is the weighted accuracy and UA is the unweighted accuracy. Now, the RAVDESS [33] dataset is frequently used in sentiment speech and song recognition systems to test its meaning and effectiveness. Table 4 presents the experimental results of various benchmark models on the dataset RAVDESS, including weighted, unweighted, etc., evaluation metrics to show the strength of the proposed method. From the experimental data, it can be seen that our proposed method significantly outperforms other benchmark models in various metrics. Figure 8 shows the recognition accuracy of our proposed model for different emotions on the dataset RAVDESS. In particular, our method achieves F1_scores of 91% and 90% on the angry and calm emotion classification tasks. The poor performance on the neutral emotion classification task may be due to the fact that the sample rate of this type of emotion in the dataset is too small and under-represented. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Year** & **WA (\%)** & **UA (\%)** & **Acc. (\%)** \\ \hline CNN+BiLSTM & 2020 & 81 & 77 & 77 \\ BiLSTM+DBN & 2021 & - & - & 77 \\ DCNN & 2020 & - & - & 77.6 \\ DSCNN & 2019 & 68 & 61 & 70 \\ HA-ACNN & 2021 & 77.8 & 77.4 & - \\ ConvLSTM+GRUs & 2020 & 81 & 80 & 80 \\ Bag-SVM & 2020 & - & - & 75.7 \\ LSTM-Transformer & 2020 & 77.3 & 75.6 & - \\ Conformer+AUGRUs (ours) & 2022 & 82.5 & 80.3 & 81 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy summary of various models obtained using RAVDESS database. WA is the weighted accuracy and UA is the unweighted accuracy. Figure 7: Recognition of different emotions by the proposed method. Through experiments, a comparison with the method of [43] shows that there is a large gap between models of deep learning and machine learning models in terms of extraction of feature information and accuracy of analysis results.