Spoken Language Biomarkers for Detecting Cognitive Impairment
Spoken Language Biomarkers for Detecting Cognitive Impairment


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.


Tuka Alhanai is a PhD candidate in the department of Electrical Engineering and Computer Science at MIT, where she focuses on the development of machine learning algorithms in the context of speech and language processing. Tuka’s current work leverages multi-modal data to develop automated tools that assess an individual's emotional and mental well-being, such as depression and dementia, and is currently collaborating with the Framingham Heart Study in this line of research. Her work has been published in top artificial intelligence venues, and covered by several media outlets including BBC, Newsweek, Wired, and TechCrunch.

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