Enhancing children’s emotional education with speech emotion recognition using deep learning

Speech emotion recognition (SER) plays a pivotal role in advancing affective computing and intelligent human–computer interaction, particularly in the context of children’s emotional communication. Traditional SER systems relying on handcrafted acoustic features often struggle to capture the complex temporal and spectral patterns inherent in emotional speech. To overcome these limitations, this study proposes a hybrid convolution-transformer framework based on the Vision Transformer (ViT) architecture for recognizing emotions from children’s speech. The proposed method first converts audio signals into mel-spectrograms and then utilizes ViT’s global self-attention mechanism to model both local spectral cues and long-range temporal dependencies. To ensure comprehensive validation, we conducted multi-level benchmarking experiments against classical machine learning models, deep learning baselines, and advanced architecture for speech emotion recognition. Experimental results demonstrate that the proposed ViT-based approach achieves competitive performance while maintaining a lightweight architecture. This balance between computational efficiency and modeling power highlights the potential of ViT for real-world SER applications.