Applying a Convolutional Vision Transformer for Emotion Recognition in Children with Autism: Fusion of Facial Expressions and Speech Features

With advances in digital technology, including deep learning and big data analytics, new methods have been developed for autism diagnosis and intervention. Emotion recognition and the detection of autism in children are prominent subjects in autism research. Typically using single-modal data to analyze the emotional states of children with autism, previous research has found that the accuracy of recognition algorithms must be improved. Our study creates datasets on the facial and speech emotions of children with autism in their natural states. A convolutional vision transformer-based emotion recognition model is constructed for the two distinct datasets. The findings indicate that the model achieves accuracies of 79.12% and 83.47% for facial expression recognition and Mel spectrogram recognition, respectively. Consequently, we propose a multimodal data fusion strategy for emotion recognition and construct a feature fusion model based on an attention mechanism, which attains a recognition accuracy of 90.73%. Ultimately, by using gradient-weighted class activation mapping, a prediction heat map is produced to visualize facial expressions and speech features under four emotional states. This study offers a technical direction for the use of intelligent perception technology in the realm of special education and enriches the theory of emotional intelligence perception of children with autism.