# Speech Emotion Recognition: Comparative Analysis of CNN-LSTM and Attention-Enhanced CNN-LSTM Models
## 4 Results and Discussion
Model 1, trained with uniform noise, achieved a notable accuracy of 96.5%, with the highest performance in identifying emotions such as _happy_, _angry_ and _others_. Model 2, which was further enhanced with an attention layer and trained using AWGN, achieved an improved accuracy of 98.1%. It demonstrated stronger generalization and better distinction between closely related emotional states such as _neutral_ and _calm_, while maintaining high accuracy across all emotion classes. The comparison Table 2 highlights the training efficiency and accuracy across different epochs, presenting the steady improvement and stabilization of model performance over time. The convergence rate is also very high. The performance metrics Table 3 shows that Model 2 achieves a better balance in precision, recall, and F1 score. This improvement is due to the attention mechanism's ability to highlight important features in the data. The detailed examination of these metrics shows that while both models perform well, the addition of attention mechanisms in Model 2 significantly enhances performance, especially in accurately classifying nuanced emotional expressions. Figure 6 presents the training and validation losses for both Model 1 (a) and Model 2 (b) before and after the addition of noise augmentation. The figure illustrates a notable contrast in training and validation losses before and after the introduction of noise. Initially, both models displayed high training loss that gradually decreased, indicating stabilization through learning. However, after a few epochs, both models experienced an increase in validation loss, indicating potential overfitting. One important thing to note here is that the second model has smoother graphs compared to the first model. Additionally, the second model exhibits higher validation loss before the addition of noise. This suggests that the model is effectively memorizing the data but struggling with the underlying patterns. This \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Models** & **Epochs** & **Training Time (Minutes)** & **Accuracy** \\ \hline Model 1 & 60 & 20 & 60\% \\  & 130 & 43 & 80\% \\  & 200 & 71 & 96.5\% \\ \hline Model 2 & 60 & 31 & 67\% \\  & 130 & 68 & 85\% \\  & 200 & 103 & 98.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A comparison of the training epochs, time, and accuracy of both models. overfitting might be attributed to the complexity of the model as the dataset is well-balanced and data mismatch is not the primary cause. This study highlights the importance of architectural choices in deep learning models for SER, advancing the field beyond prior works (Table 1). Compared to [17] (64.68% on RAVDESS), our models achieve superior accuracy (96.5% and 98.1%) due to uniform noise, AWGN, and attention enhancements, unlike Google's model, which lacks noise robustness [33]. The organization of CNN and LSTM blocks within a network is important, as the unique capabilities of each layer must be effectively utilized. CNN layers extract spatial features from spectrograms, while LSTM units capture the temporal dependencies essential for SER. The sequence and interaction between these blocks determine the model's overall learning effectiveness and feature integration. Additionally, incorporating attention mechanisms can improve the network's focus, allowing for a more detailed analysis of emotional cues. Ultimately, the configuration and composition of the network architecture play a critical role in the model's ability to achieve high accuracy in SER tasks. Table 1 in the literature review section presents notable prior work in the field of SER. Comparing our results with those studies shows that our proposed models, especially Model 2, perform exceptionally well in terms of accuracy and generalization. For instance, Ref. [17] used ResNets and achieved an accuracy of 64.68% on RAVDESS. Ref. [19] applied CNN-LSTM on the same dataset and reported 90% accuracy. Similarly, Ref. [16] using CNN achieved 92%.