Multiangle Correlation Feature Extraction and Disease Prediction Model Construction for Patients With Post-Stroke Dysarthria.

The clinical diagnosis and treatment of motor dysarthria in post-stroke patients is often subjective and neglects the impact of psychological and emotional disorders on disease progression. This study aims to analyze the correlation among emotional expression, psychological state, facial expression, and dysarthria disease severity and is dedicated to the construction of a dysarthria prediction model. We first designed THE-POSSD, a novel Chinese multimodal emotional pathology expression database, which collected acoustic, glottal, and facial data under emotional stimuli from patients at different disease stages and healthy controls. Emotional speech was labeled for intelligibility scores, emotion types, and discrete dimensional space. Then, their correlation with disease development was investigated and analyzed. A total of 154 significant correlation features were extracted for analysis. To mitigate the limitations of subjective clinical scale diagnosis and account for psychological and emotional factors, this study introduced the grey correlation theory and constructed a dysarthria prediction model based on the grey relational analysis-deep belief network (GRA-DBN). Principal Component Analysis and Variance Inflation Factor were employed to optimize GRA-DBN model. Both proposed models achieved a high prediction accuracy, with an adjusted R2 value of 0.85 for GRA-DBN and 0.92 for optimised model. This study fills the gap in the international multimodal emotional pathological expression dataset and provides a comprehensive framework for analyzing the association between mental state, emotional expression, and the degree of dysarthria. Furthermore, the incorporation of key multimodal features into the predictive model highlights its potential to enhance the precision of clinical diagnostic processes significantly.