AI-Driven Detection of Neurodevelopmental Disorder from Emotional Speech Using a Hybrid CNN–BiLSTM–Attention Framework

Neurodevelopmental disorders (NDDs) are associated with impairments in communication, behavior, and social interaction, making accurate diagnosis clinically challenging. Autism Spectrum Disorder (ASD), a major NDD, often exhibits atypical speech patterns characterized by altered prosody and reduced emotional expressiveness. The study proposes a hybrid dual-path framework for ASD detection from emotional speech using two strategies: PCA–GMM-based acoustic modeling and a CNN–BiLSTM–Attention architecture for spectral–temporal feature learning. The proposed framework captures probabilistic, spectral, and temporal speech characteristics for robust ASD classification. Acoustic analysis demonstrated clear separability between ASD and non-ASD speech, while the deep learning framework achieved stable and reliable performance across multiple emotional conditions. Experimental evaluation achieved 98.3% accuracy, AUC values ranging from 0.9699 to 0.9864, and F1-scores up to 0.9891. The findings highlight the potential of AI-driven speech analysis as a scalable and non-invasive tool for early ASD screening and predictive healthcare applications.