# Speech Emotion Recognition: Comparative Analysis of CNN-LSTM and Attention-Enhanced CNN-LSTM Models
## 4 Results and Discussion  
This section evaluates the models' performances both qualitatively and quantitatively using metrics such as loss, accuracy, precision, recall, and F1 score. Confusion matrices are used to effectively compare the classification capabilities of both models. In this study, noise augmentation was applied to both models using two different techniques. Model 1 was trained with uniformly distributed noise added to the signal using a custom uniform noise injection method, while Model 2 was augmented using additive white Gaussian noise (AWGN), as described in the provided implementation. The augmentation methods improve generalization and reduce overfitting, all without modifying the underlying model architecture. To ensure fair evaluation and avoid time-out issues, training was conducted on a GPU (NVIDIA RTX 3080) with a batch size of 32, completing 564 epochs within the reported times (Table 2). There are various techniques available for improving generalization, such as regularization, early stopping, and simplifying the model by removing layers or blocks. However, in this study, we opted to keep the model unchanged, using simple noise augmentation to maintain overall simplicity. Both uniform noise and AWGN contributed to a significant reduction in validation loss, suggesting improved model generalization. Confusion matrices are essential for evaluating classification tasks. Figure 5 provides a comprehensive comparison of both models, before and after noise augmentation, as well as their final test performance. Figure 5a,b show the validation results of Model 1 before and after adding uniform noise, respectively. Initially, Model 1 struggles to differentiate between emotionally similar classes like _sad_ and _calm_, as seen in Figure 5a. After uniform noise augmentation, Figure 5b shows significant improvements across most emotions, especially in reducing misclassification of _neutral_, which had fewer training samples. Figure 5c,d present the validation confusion matrices for Model 2 before and after applying additive white Gaussian noise (AWGN). While Figure 5c shows relatively better initial performance compared to Model 1, confusion still exists between classes such as _surprise_, _fear_, _disgust_, and _calm_. After applying AWGN, as shown in Figure 5d, Model 2 exhibits clearer class boundaries, with improved classification across all emotional states. Figure 5e,f display the final test set confusion matrices for both models. Model 1 (Figure 5e), trained with uniform noise, achieves a solid accuracy of 96.5%, correctly predicting dominant emotions like _happy_, _angry_ and _others_. Model 2 (Figure 5f), trained  
Figure 5: Confusionmatrices showing performance before and after applying uniform noise for both models, along with final test set performance. Each subfigure is labeled (**a**) through (**f**) to indicate the following: (**a**) Model 1 validation before noise; (**b**) Model 1 validation after noise; (**c**) Model 2 validation before noise; (**d**) Model 2 validation after noise; (**e**) Model 1 final test performance; (**f**) Model 2 final test performance. with AWGN and enhanced with attention mechanisms, reaches a peak accuracy of 98.1%, with very few misclassifications and strong generalization across the emotional spectrum. These matrices confirm the contribution of both uniform noise and AWGN-based augmentation, along with architectural design, toward improved emotion recognition performance. \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{25}\]  
\[Precision=\frac{TP}{TP+FP} \tag{26}\]  
\[Recall=\frac{TP}{TP+FN} \tag{27}\]  
\[F1 Score=\frac{2\cdot Precision\cdot Recall}{Precision+Recall} \tag{28}\]  
These metrics provide comprehensive details about the performance of classification models, helping to assess their ability to correctly classify emotions and balance precision and recall [32]. The confusion matrices provide insight into the predictive accuracy of each model across various emotional states.