## 7 Discussion and Analysis
### Visualization of Emotional Features in Autistic Children
The confusion between calm and sad emotional states represents the most frequent error pattern in our facial expression recognition system, with bidirectional misclassifications accounting for approximately 4.6% of errors across these categories. In cases where the model incorrectly classified sad expressions as calm, the Grad-CAM visualizations typically showed insufficient attention to subtle mouth configurations while maintaining focus on the eye regions. This pattern suggests that the model occasionally struggles to integrate multiple facial cues when expression intensity is reduced, a common characteristic in children with autism who often exhibit flattened affect. In contrast, happy expressions were rarely confused with other emotions (misclassification rate of only 2.8%), likely due to the distinctive activation patterns around the mouth region that our model successfully captured through the attention mechanism. Figure 14 presents the Grad-CAM heat maps highlighting the critical time-frequency regions of Mel spectrograms that significantly influence the model's emotion classification decisions. The intensity of the red coloration corresponds to the degree of feature importance, with darker regions indicating stronger activation in the model's decision-making  
process. Our comprehensive analysis reveals distinctive attention patterns across the four emotional states as follows:  
1. For calm emotional states, the model predominantly attends to low-frequency components and regions of minimal energy variation. This concentration of attention corresponds to the acoustic characteristics of calm speech in children with autism, which typically exhibits minimal spectral variation and more stable frequency patterns across time. 2. In happy emotional expressions, the model directs significant attention to regions of elevated energy in the middle and low frequency bands. The heat map reveals concentrated activation in areas with distinctive tonal qualities and increased spectral energy density. 3. For sad emotional states, the model identifies and emphasizes regions exhibiting irregular frequency distributions and notable temporal fluctuations. The heat map demonstrates focused on transitional components in the spectrogram, indicating the model's sensitivity to the distinctive rhythm and prosodic features. 4. In angry emotional expressions, the heat map exhibits pronounced activation in high-frequency and high-energy regions of the spectrogram. This pattern of attention demonstrates the model's ability to identify the increased speech signal energy and intensity characteristic of angry vocalizations. The examination of speech emotion recognition errors revealed that angry vocalizations were occasionally misclassified as sad (approximately 6.2% of cases). In these instances, the Grad-CAM visualizations showed the model attending to lower frequency regions that typically characterize sad expressions rather than the high-energy, high-frequency components more common in angry vocalizations. This finding suggests that the model struggles with distinguishing between angry vocalizations with suppressed intensity (common in children with autism who may internalize anger) and sad emotional expressions. This pattern aligns with clinical observations that children with autism may express anger differently than typically developing children, often with atypical vocal characteristics that blend emotional categories that are more distinct in neurotypical populations. Based on the aforementioned analysis, it is concluded that the speech characteristics associated with happy, sad, angry, and calm emotions in children with autism are significantly different, primarily in terms of frequency and energy distribution. Figure 14: Grad-CAM visualization of Mel spectrogram features across four emotional states (calm, happy, sad, and angry) in children with autism. The intensity of the red coloration indicates regions of high importance for the model’s decision-making process, with darker red representing stronger feature activation.