Paediatric-HGNN: A Hybrid Heterogeneous Graph Neural Network for Detecting Disfluency in Children's Speech via Multiscale Acoustic Fusion
Researchers have developed a new framework called Paediatric-HGNN, which utilizes a hybrid heterogeneous graph neural network to detect disfluencies in children's speech. This approach models hierarchical relationships between words and acoustic segments, aiming to better distinguish pathological stuttering from typical developmental speech patterns. When tested on specific pediatric corpora, the system achieved an 82.4% weighted accuracy and a Typical Disfluency F1-score of 0.386, offering potential for early clinical intervention. AI
IMPACT This model could improve early diagnosis and intervention for speech disorders in children.