CPFformer: A Hierarchical-Based Graph Modeling Fusion Framework for Making the Emotional Features of Chinese Poetry Pronunciation More Controllable.

Abstract

Chinese poetry, a pinnacle of cultural expression, encapsulates human emotions and societal narratives in succinct, evocative language. Its unique blend of linguistic constraints and musicality makes analyzing its pronunciation's emotional features crucial for enriching children's linguistic prowess and artistic appreciation. To this end, we propose CPFformer, a novel deep learning framework, merges phonetics, sentiment analysis in order to analyze and predict the emotional feature of Chinese poetry pronunciation effectively. CPFformer, which consists of anomaly detection in spatial network (ADSN), spatial-temporal learning (STL), dimension segmentation and embedding (DSW), extraction of temporal attention (ETA), and encoder-decoder module (EDSM) modules, employs graph structures to capture the global and local consistency of emotional features across spatial and temporal, and a multiscale Mel feature extraction technique ensures comprehensive analysis of speech dynamics, enhancing emotional expression understanding. The mean square error (mse), mean absolute error (MAE), residual standard error (RSE), and $R$ -square ( $R^{2}$ ) of experiments reach 0.3572, 0.2486, 0.2014, and 0.9822, respectively, demonstrating its feasibility and effectiveness, exhibiting its superiority to the state-of-the-art approaches. The creation of a dedicated Chinese poetry pronunciation dataset marks a significant contribution, facilitating further research. The potential of CPFformer in speech technology and education heralds a new era, fostering the integration of traditional culture and artificial intelligence, and promoting the advancement of emotional literacy and smart learning environments. Its interdisciplinary implications promise exciting avenues for research and application.