Spoken language biomarkers for detecting cognitive impairment

Tuka Alhanai, Rhoda Au, James Glass

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2017 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages409-416
Number of pages8
ISBN (Electronic)9781509047888
DOIs
StatePublished - Jan 24 2018
Event2017 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2017 - Okinawa, Japan
Duration: Dec 16 2017Dec 20 2017

Publication series

Name2017 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2017 - Proceedings
Volume2018-January

Conference

Conference2017 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2017
CountryJapan
CityOkinawa
Period12/16/1712/20/17

Keywords

  • cognitive impairment
  • elastic-net
  • feature selection
  • regression
  • spoken language

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

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  • Cite this

    Alhanai, T., Au, R., & Glass, J. (2018). Spoken language biomarkers for detecting cognitive impairment. In 2017 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2017 - Proceedings (pp. 409-416). (2017 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2017 - Proceedings; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASRU.2017.8268965