## 1 Introduction  
Children with autism spectrum disorder (ASD) experience impairments in their social-emotional functioning and abnormalities in their emotional development compared with typically developing children. Children with autism have difficulties in recognizing and understanding others' emotions and in processing their own emotions [1]. Typically, they exhibit inadequate or excessive emotional reactions [2] and cannot convey their feelings in a way that is recognizable and comprehensible to other people, which can lead to severe problems in social interactions [3] and may consequently affect their mental health. Therefore, accurately identifying and understanding the emotions of children with autism is essential for developing targeted interventions and support strategies. However, at present, the emotional states of children with autism are predominantly evaluated and interpreted by trained therapists or psychologists, which can be a challenging and long-term process for inexperienced families and intervenors. Existing research on the emotions of children with autism primarily focuses on comparing the emotion recognition abilities of children with autism with those of typically developing children. There is little research on the external emotional expressions and internal emotional activities of individual children with autism. In typically developing children's learning affective computing domain, many studies have tracked the changes in individual learning emotions [4; 5]. These studies involve creating datasets of children's emotions and employing deep learning technology to construct models for recognizing emotions [6], thereby enabling the intelligent detection of children's learning emotions. However, compared with those of typically developing children, the facial muscle movements of children with autism are different [7], and their facial expressions exhibit asynchrony [8] and a lower complexity [9]. These findings lead to the assumption that affective computing models and datasets for the general population cannot be directly adapted and applied to this special group of children. In addition, the language development ability of children with ASD, who have fewer verbal expressions and more emotional elements in their speech, lags behind that of typically developing children. For example, special pronunciations such as \"babbling\" and \"growing\" contain negative emotions. Therefore, the speech modality can be used as a key reference for emotion recognition of children with ASD. The current research on the automatic emotion recognition of children with ASD faces the following limitations:  
1. The existing studies mainly rely on single-modal data such as facial expressions to analyze the emotions of children with ASD. This approach may not comprehensively capture these children's emotional states. For example, the emotional expression of happiness is typically accompanied by multiple simultaneous responses such as smiling, high pitch, and quick speech. To enhance the accuracy and robustness of emotion recognition, the emotional information from the facial expressions and speech of children with autism can be cross-corroborated and compensated for. Therefore, the construction of multimodal datasets plays a crucial role in the emotion recognition of children with autism. 2. Emotion recognition algorithms have low accuracies. Traditional machine learning methods such as the support vector machine (SVM) or decision tree have limited performance in processing complex and high-dimensional data. The commonly employed deep learning model convolutional neural network (CNN) for emotion computation, which relies on local facial cues, cannot capture the interplay between different facial regions from a holistic viewpoint. The vision transformer model can effectively compute and assign weights to different facial regions to extract highly discriminative features from facial images. This capability enables the vision transformer to meet the specific requirements for emotion computation for children with autism.