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Predicting Mini-Mental Status Examination Scores through Paralinguistic Acoustic Features of Spontaneous Speech

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

Original languageEnglish
Title of host publication42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)978-1-7281-1990-8
DOIs
Publication statusPublished - 27 Aug 2020
Event42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society : EMBC'20 - Palais des congrès de Montréal, Montréal, Québec, Canada
Duration: 20 Jul 202024 Jul 2020

Conference

Conference42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Period20/07/2024/07/20

Abstract

Speech analysis could provide an indicator of cognitive health and help develop clinical tools for automatically detecting and monitoring cognitive health progression. The Mini Mental Status Examination (MMSE) is the most widely used screening tool for cognitive health. But the manual operation of MMSE restricts its screening within primary care facilities. An automatic screening tool has the potential to remedy this situation. This study aims to assess the association between acoustic features of spontaneous speech and assess whether acoustic features can be used to predict automatically MMSE score. We assessed the effectiveness of paralinguistic feature set for MMSE score prediction on a balanced sample of DementiaBank's Pitt spontaneous speech dataset, with patients matched by gender and age. Linear regression analysis shows that fusion of acoustic features, age, sex and years of education provides better results (mean absolute error, MAE = 4.97, and $R^2 = 0.261$) than acoustic features alone (MAE = 5.66 and $R^2 =0.125$) and age, gender and education level alone (MAE of 5.36 and $R^2= 0.17$). This suggests that the acoustic features of spontaneous speech are an important part of an automatic screening tool for cognitive impairment detection.

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