## 6 Results
### Comparison with Existing Methods  
To clearly position our contribution within the field, we present a comparative analysis between our approach and the existing emotion recognition methods for children with autism, as shown in Table 10.  
Unlike previous approaches, our method introduces three key innovations that directly contribute to its superior performance as follows:  
1. Utilization of CvT architecture: While previous studies like that of [PERSON] et al. (2021) [20] primarily employed CNN-based architectures for facial emotion recognition, our approach leverages the hybrid CvT architecture. This design choice is particularly relevant for children with autism, who display reduced dynamic complexity in facial expressions. The CvT architecture integrates convolutional operations with transformer mechanisms through the following two critical components: convolutional token embedding and convolutional projection. The convolutional token embedding captures local facial muscle movements--particularly important since children with autism show different muscle activation patterns--while the transformer component models relationships between distant facial regions. Our comparative experiment in Table 7 quantitatively demonstrates this architectural advantage, with the CvT model achieving 79.12% accuracy compared to the pure transformer approach (ViT) at 48.00%. This substantial 31.12% improvement confirms that local feature extraction through convolution, when combined with global attention mechanisms, is essential for accurately recognizing the often subtle and atypical facial expressions of children with autism. Further analysis reveals that the CvT architecture particularly improved the recognition of happy emotions (90.14% vs. 52.57% with ViT) due to its ability to capture the distinctive eye and mouth coordination patterns that characterize happiness in children with autism. 2. Attention-based fusion strategy: Previous multimodal approaches such as [PERSON] et al. [34] employed simple concatenation or averaging of features. In contrast, our approach uses a sophisticated Multi-scale Channel Attention Module (MS-CAM) that dynamically weights features from different modalities. This is crucial because children with autism often display asynchronous emotional expressions [8], where facial expressions and vocal cues may not align temporally or in intensity. The MS-CAM module, detailed in Equation (7), computationally determines optimal feature importance through parallel processing branches as follows: the global attention branch captures overall emotional states using global average pooling, while the local attention branch preserves spatial information critical for detecting localized emotional cues. As shown in Table 8, our fusion approach achieves balanced performance across all emotion categories (precision ranging from 88.39% to 91.75%), demonstrating its ability to adaptively emphasize the most informative modality for each emotion type. Particularly for sad emotions, where facial expressions might be subtle but vocal characteristics distinctive, the fusion model achieves 91.14% precision compared to 75.67% and 82.15% with single-modal approaches. This balanced improvement \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Modalities** & **Architecture** & **Key Features** & **Reported Accuracy** \\ \hline [PERSON] et al. (2021) [34] & Visual + Audio & CNN + LSTM & Game therapy scenario & 72.40\% \\ [PERSON] et al. (2021) [20] & Facial & Inception-ResNet-V2 & Meltdown detection & 76.50\% \\ [PERSON] et al. (2021) [24] & Speech & MLP & Seven basic emotions & 71.30\% \\ Our Method & Facial + Speech & CvT + Attention & Multimodal fusion with MS-CAM & 90.73\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison with Existing Emotion Recognition Methods for Children with Autism. across emotion categories provides strong evidence that the attention-based fusion mechanism effectively addresses the heterogeneous nature of emotional expressions in children with autism.