Asian Journal of Education and Social Studies

Background and Aims: Cognitive impairment in the elderly is a major global public health challenge. Traditional neuropsychological assessments suffer from subjectivity, educational bias, and limited sensitivity for early detection. Speech production relies on complex neural networks, and cognitive decline often first manifests as alterations in speech prosody. This paper aims to construct an intelligent diagnostic model based on prosodic analysis for early detection of cognitive impairment.

Methodology: The model integrates neurolinguistic principles, signal processing methods, and machine learning. It extracts multi-dimensional prosodic features: temporal rhythm metrics, pitch dynamic features, energy distribution features, rhythm formant-based modulation spectrum features, and deep features from prosodic spectrograms using a Vision Transformer (ViT). Feature selection strategies, classifier optimization, and multimodal fusion with lexical features are also included.

Results: Based on validation from existing empirical studies reviewed in this paper, similar models have achieved classification AUC above 0.80 for mild cognitive impairment versus healthy controls, and F1 scores above 0.90 for Alzheimer's disease. Automated pause analysis has shown significant correlation with tau and amyloid neuropathological markers.

Conclusion: The proposed model provides a non-invasive, low-cost, interpretable tool for community-based screening and clinical decision support. Future research requires cross-linguistic validation, longitudinal data collection, and clinical deployment.