@inproceedings{10d2c6fcfa3c4a7bb03d2c6474e1f18c,
title = "Spoken language biomarkers for detecting cognitive impairment",
abstract = "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.",
keywords = "cognitive impairment, elastic-net, feature selection, regression, spoken language",
author = "Tuka Alhanai and Rhoda Au and James Glass",
year = "2018",
month = jan,
day = "24",
doi = "10.1109/ASRU.2017.8268965",
language = "English (US)",
series = "2017 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2017 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "409--416",
booktitle = "2017 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2017 - Proceedings",
note = "2017 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2017 ; Conference date: 16-12-2017 Through 20-12-2017",
}