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Thus, this research has focused on evaluating the effects of the above-mentioned factors on the performance of ML classifiers that can be used for dementia assessment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methodology<\/jats:title><jats:p>Our methodology includes the following steps: (1) Collecting speech and language datasets from patients and healthy controls; (2) Using feature engineering methods which include feature extraction methods to extract linguistic and acoustic features and feature selection methods to select most informative features; (3) Training different ML classifiers; and (4) Evaluating the performance of ML classifiers to investigate the impacts of language tasks, recording media, and modalities on dementia assessment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Our results show that (1) the ML classifiers trained with the picture description language task perform better than the classifiers trained with the story recall language task; (2) the data obtained from phone-based recordings improves the performance of ML classifiers compared to data obtained from web-based recordings; and (3) the ML classifiers trained with acoustic features perform better than the classifiers trained with linguistic features.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This research demonstrates that we can improve the performance of automatic SLAMs as dementia assessment methods if we: (1) Use the picture description task to obtain participants\u2019 speech; (2) Collect participants\u2019 voices via phone-based recordings; and (3) Train ML classifiers using only acoustic features. 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