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Updated: Mar 19, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Database of Speech and Language Affected by Mild Cognitive Impairment
Milan Rusko1, Petra Brandoburová2,3,4, Viktória Kevická5,6,7
1Institute of Informatics of the Slovak Academy of Sciences, Bratislava, Slovakia.
None:
Mild Cognitive Impairment (MCI) is increasingly recognized as an early indicator of neurodegenerative diseases, including Alzheimer's disease (AD). Speech and language deficits are known to emerge already in the MCI stage of various aetiologies. The ALOIS database (ALOIS-DB) was created to support research and development in this area by creating and providing a corpus of speech from Slovak-speaking individuals diagnosed with MCI and cognitively healthy controls. The dataset includes recordings from 258 participants (102 MCI, 156 controls), collected through a battery of nine speech and language tasks targeting various speech and language domains. All recordings were obtained using a custom-developed tablet application and were transcribed and annotated according to established protocols. The resulting dataset is accompanied by metadata and is publicly available for research purposes. ALOIS-DB represents a valuable resource for developing automated tools for the detection of early cognitive decline and contributes to the growing body of speech data relevant to neurocognitive disorders.
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