## 7 Discussion and Analysis
### Limitations and Future Work
Our data collection approach in semi-natural settings, while more ecologically valid than posed expressions, still occurred within structured rehabilitation environments, potentially missing emotional expressions that emerge in less constrained contexts. This methodological limitation may affect the model's performance in real-world applications where environmental factors and social contexts differ significantly from training conditions, introducing additional sources of error not accounted for in our controlled evaluation. Subsequent studies should gather data from diverse countries and regions with larger sample sizes to further validate the model's applicability and universal effectiveness across different cultural contexts and autism presentation variations, while also incorporating a wider range of emotional states and purposefully sampling children across different points of the autism spectrum to better capture the heterogeneity of autism expression. 3. Model Architecture Considerations  
While we selected CvT as our primary model architecture due to its performance advantages, future work could explore more recent transformer variants such as the Swin Transformer with its hierarchical representation capabilities or ConvNeXt, which combines CNN efficiency with transformer-like performance. These architectures may offer additional performance improvements for autism-specific emotion recognition tasks, potentially addressing some of the current model's limitations in distinguishing between similar emotional states. Our current architecture processes individual frames rather than temporal sequences, missing potentially important information about expression velocity and coordination that characterize emotional states in autism. The fusion approach employs fixed attention weights after training, lacking adaptability to adjust feature importance dynamically for individuals with unique expression patterns. These architectural limitations could be addressed in future research through temporal modeling and adaptive fusion mechanisms. 4. Model Analysis and Optimization  
The current study did not conduct comprehensive ablation studies to determine the exact contribution of each model parameter to the overall performance. Future research should investigate the sensitivity of model performance to key parameters such as the number of attention heads, convolutional kernel sizes, and hidden layer dimensions to optimize the architecture specifically for emotion recognition in children with autism. Additionally, our performance evaluation focused primarily on accuracy metrics rather than interpretability or computational efficiency. Future work should explore techniques such as knowledge distillation, structured pruning, and quantization to enhance computational efficiency while preserving the high accuracy essential for effective emotion recognition in autism intervention settings. 5. Speech Feature Exploration  
In speech signal modalities, only the most typical features of the Mel spectrogram and MFCCs are taken into account, while other features such as the linear prediction cepstrum coefficients (LPC), wavelet transforms, line-spectrum pair parameters, and amplitude ratios are not investigated. The impacts of individual features and feature combinations on the efficacy of emotion recognition models should be further examined in future research, and experiments are required to identify the speech elements that most accurately represent the emotions associated with autism. Future work should also explore end-to-end models like WaveNet for speech processing, which might capture temporal dynamics more effectively. 6. Computational Efficiency Optimization  
While our multimodal fusion approach achieves superior accuracy, its practical deployment in resource-constrained educational environments requires further optimization.