Automatic Classification of Dementia Using Text and Speech Data

Hee Jeong Han, Suhas B. N, Ling Qiu, Saeed Abdullah

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations


Dementia is a serious public health concern. It does not have any cure. Early detection of dementia is, thus, critical for effective symptom management as well as delaying cognitive and functional decline. This paper focuses on detecting onset of dementia using text and speech features provided by two publicly available datasets from the AAAI 2022 hackallenge. Our approach resulted in developing ACOUSTICS (AutomatiC classificatiOn of sUbjectS with demenTIa and healthy Controls using text transcriptions and Speech data)—an ensemble model with two deep learning-based architectures for text and speech analysis. ACOUSTICS achieved 89.8% accuracy when classifying individuals with dementia and health controls. Our approach outperforms current state-of-the-art methods in dementia detection.

Original languageEnglish (US)
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages9
StatePublished - 2023

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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