# Multi-Level Attention-Based Categorical Emotion Recognition Using Modulation-Filtered Cochleagram
## 4 Experimental Results and Analysis
### Experimental Results Analysis
Moreover, the utilization of the attention method outperforms the deep model with the max-pooling operation, indicating that attention can capture discriminative emotional information from high-dimensional spatial information. Furthermore, the results also show that the multi-level attention network outperforms other models, achieving the highest UA of 71.0% and F1 score of 69.2%. In comparison to the 3D CRNN attention model, the proposed multi-level attention network exhibits a considerable absolute improvement of +3.2% and +3.8% in UA and F1-score metrics, respectively. Similarly, when compared to the triple attention model, the multi-level attention network shows an absolute improvement of +1.6% and +1.1% in UA and F1-score metrics, respectively. These results suggest that the proposed method with the multi-level attention network is capable of extracting more informative features from speech data compared to the 3D CRNN attention and triple attention models. This indicates the superiority of the multi-level attention network in recognizing emotions, which can \begin{table}
\begin{tabular}{c c} \hline \hline
**FEATURE** & **UA** \\ \hline MFCC & 58.5 \\ embose2010 & 60.9 \\ IS09 & 59.4 \\ MSF & 59.7 \\ Spectrogram & 61.6 \\ Cochleagram & 62.1 \\ MCG & 63.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison between different features on the IEMOCAP database (%). \begin{table}
\begin{tabular}{c c c} \hline \hline
**METHOD** & **UA** & **F1 Score** \\ \hline
3D CRNN-max-pooling & 67.5 & 64.2 \\
3D CRNN-attention & 67.8 & 65.4 \\ Triple-attention & 69.4 & 68.1 \\ Proposed method & 71.0 & 69.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison between different architectures on the IEMOCAP database (%). use a channel and spatial attention to obtain complementary attention maps and use temporal attention to obtain significant emotional regions. It can capture salient emotional information from the multi-dimensional space of the MCG feature and greatly reduce the parameters of conventional neural networks, which is important for high-dimensional representation space. Overall, these findings support the efficacy of the proposed multi-level attention network in emotion recognition tasks, showcasing its potential for improving the accuracy and interpretability of emotion recognition systems. In summary, Table 2 provides strong evidence for the improved performance of the proposed architecture in emotion recognition, outperforming other models in terms of both UA and F1 score. These statistical findings emphasize the effectiveness of the multi-level attention network, along with the advantages of incorporating 3D CNN, max-pooling, and attention mechanisms in recognizing emotions from MCG features. Figure 8 presents a confusion matrix obtained from the experiments. The experimental results reveal that the proposed method obtains the highest recognition rate for sad and the lowest recognition rate for neutral emotion. There is a tendency for confusion between sad and neutral emotions, with instances of misclassification in both directions. Additionally, anger is more easily confused with happy than happy is confused with anger. In general, the ability of the multi-level attention model based on MCG features to recognize emotions is the same as that of the human auditory system. To show the benefit of the proposed model, we compare our results with the studies presented in Table 3. In [34], the authors proposed an end-to-end speech emotion recognition system using multi-level acoustic information, including MFCC, spectrogram, and wav2 vec2, along with a newly designed co-attention module. In [39], the authors used log-Mel filterbank features as the input to an autoencoder and used an attentive CNN for representation learning. In [40], the authors used a 3D attention-based CRNN for learning discriminative features for SER, utilizing a Mel spectrogram with deltas and delta-deltas as inputs.