ArticleFrontiers in aging neuroscience2023
The efficacy of memory load on speech-based detection of Alzheimer's disease.
Article in Frontiers in aging neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Linguistic features from paragraph recall are markers of cognitive impairment.Journal of the International Neuropsychological Society : JINS · 2026Article
- Integrating acoustic, prosodic, and phonological features for automatic Alzheimer's detection.Frontiers in aging neuroscience · 2026Article
- Early ultrasonic vocalization deficits and related thyroarytenoid muscle pathology in the transgenic TgF344-AD rat model of Alzheimer's disease.Frontiers in behavioral neuroscience · 2023Article
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Authors and funding
6 authors.
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Abstract
Introduction: The study aims to test whether an increase in memory load could improve the efficacy in detection of Alzheimer's disease and prediction of the Mini-Mental State Examination (MMSE) score. Methods: Speech from 45 mild-to-moderate Alzheimer's disease patients and 44 healthy older adults were collected using three speech tasks with varying memory loads. We investigated and compared speech characteristics of Alzheimer's disease across speech tasks to examine the effect of memory load on speech characteristics. Finally, we built Alzheimer's disease classification models and MMSE prediction models to assess the diagnostic value of speech tasks. Results: The speech characteristics of Alzheimer's disease in pitch, loudness, and speech rate were observed and the high-memory-load task intensified such characteristics. The high-memory-load task outperformed in AD classification with an accuracy of 81.4% and MMSE prediction with a mean absolute error of 4.62. Discussion: The high-memory-load recall task is an effective method for speech-based Alzheimer's disease detection.
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