Abstract

Investigating voice abnormalities as potential biomarkers for neurodegenerative disorders may
present a cost-effective substitute for other neuro imaging techniques, bringing fresh perspectives
and enhancing diagnostic precision. The objective of this research was to pinpoint voice biomarkers
that can differentiate between healthy individuals and those suffering from dementia, as well as to
create a predictive model for Mini-Mental State Examination (MMSE) scores. Our analysis involved
studying voice recordings from 172 French speakers, who were divided into dementia, mild cognitive
impairment (MCI), and healthy control (HC) groups. By utilizing Praat and Matlab, we were able to
extract para-linguistic and linguistic speech features. Through various statistical analyses, including
ANCOVA, partial correlations, and stepwise linear regression, we were able to identify discourse
complexity, noun ratio, and average syllables per word as significant predictors of MMSE scores. Our
model, which incorporated these features, outperformed a baseline model that included covariates
such as sex, age, and education level. This demonstrated strong predictive capabilities, with an RMSE
of 3.65 and 3.7, and an R-squared of 0.35 and 0.31 on the training and test sets, respectively. This
study underscores the potential of linguistic speech features in the detection of cognitive impairment,
and emphasizes the importance of considering gender separately in the analysis to avoid bias and the
type of vocal task used in data collection.