Machine Learning

Spoken Language Biomarkers for Detecting Cognitive Impairment

This topic contains 0 replies, has 1 voice, and was last updated by  arXiv 1 month ago.


  • arXiv
    5 pts

    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.

    Spoken Language Biomarkers for Detecting Cognitive Impairment
    by Tuka Alhanai, Rhoda Au, James Glass
    https://arxiv.org/pdf/1710.07551v1.pdf

You must be logged in to reply to this topic.