TY - GEN
T1 - Spoken language biomarkers for detecting cognitive impairment
AU - Alhanai, Tuka
AU - Au, Rhoda
AU - Glass, James
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - 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.
AB - 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.
KW - cognitive impairment
KW - elastic-net
KW - feature selection
KW - regression
KW - spoken language
UR - http://www.scopus.com/inward/record.url?scp=85050668053&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050668053&partnerID=8YFLogxK
U2 - 10.1109/ASRU.2017.8268965
DO - 10.1109/ASRU.2017.8268965
M3 - Conference contribution
AN - SCOPUS:85050668053
T3 - 2017 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2017 - Proceedings
SP - 409
EP - 416
BT - 2017 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2017
Y2 - 16 December 2017 through 20 December 2017
ER -