## 5 Analysis and Discussion
### Explainability and Model Interpretation  
As the proposed speech emotion recognition (SER) system is intended for use in educational settings, it is crucial to provide insights into how the model makes decisions. Explainability and model interpretation are essential for building trust and acceptance among users, particularly educators and learners who rely on the system's emotion recognition capabilities to inform their teaching and learning strategies.  
In the current study, we have focused on developing an accurate and robust SER model using an ensemble approach combining transformer, CNN, and LSTM architectures. However, the model's decision-making process remains largely opaque, limiting users' understanding of why certain emotions are recognized in specific instances.  
To address this challenge, future work should focus on enhancing the explainability and interpretability of the SER model. Several techniques can be employed to provide deeper insights into the model's inner workings:  
Feature Importance Analysis: Conducting a thorough analysis of the importance of different audio features in the emotion recognition process can help identify the key characteristics that contribute to the model's decisions. Techniques such as permutation featureimportance or Shapley Additive Explanation (SHAP) can be used to quantify the contribution of each feature to the model's predictions [40].  
Attention Visualization: Visualizing the attention weights learned by the transformer block can provide insights into which parts of the speech signal the model focuses on when making emotion predictions. Heatmaps or other visual representations can be used to highlight the regions of the audio that are most relevant for recognizing specific emotions.  
Layer-Wise Relevance Propagation (LRP): LRP is a technique that allows for the visualization of the relevance of each input feature to the model's output. By applying LRP to the SER model [41], we can trace back the contribution of different audio features and identify the most important ones for each emotion class.  
Explainable AI Frameworks: Integrating explainable AI frameworks, such as LIME (Local Interpretable Model-Agnostic Explanation) or SHAP, can provide local explanations for individual predictions [42]. These frameworks generate interpretable explanations by perturbing the input features and observing their impact on the model's output, enabling users to understand why a particular emotion was recognized in a specific instance.  
User Studies and Feedback: Conducting user studies with educators and learners to gather feedback on the interpretability and explainability of the SER model can provide valuable insights into how well users understand and trust the model's decisions. This feedback can guide further improvements to the model's explainability and inform the development of user-friendly interfaces that effectively communicate the basis of the model's emotion recognition.  
By deepening the explainability and model interpretation of the SER system, we can foster trust and acceptance among users in educational settings. Educators and learners will be empowered to understand the underlying reasons behind the model's emotion recognition, enabling them to make informed decisions and adapt their teaching and learning strategies accordingly.