Spoken Language Biomarkers for Detecting Cognitive Impairment
This work addresses detecting cognitive impairment in clinical settings, offering a non-invasive tool, but it is incremental as it builds on existing biomarker research.
The study developed an automated system using speech and language features from audio recordings to detect cognitive impairment, achieving 0.92 AUC and a 29% true positive rate at 0% false positive rate.
In this study we developed an automated system that evaluates speech and language features from audio recordings of neuropsychological examinations of 92 subjects in the Framingham Heart Study. A total of 265 features were used in an elastic-net regularized binomial logistic regression model to classify the presence of cognitive impairment, and to select the most predictive features. We compared performance with a demographic model from 6,258 subjects in the greater study cohort (0.79 AUC), and found that a system that incorporated both audio and text features performed the best (0.92 AUC), with a True Positive Rate of 29% (at 0% False Positive Rate) and a good model fit (Hosmer-Lemeshow test > 0.05). We also found that decreasing pitch and jitter, shorter segments of speech, and responses phrased as questions were positively associated with cognitive impairment.