%0 Journal Article %1 noauthororeditor %A Thaler, Fabian %A Gewald, Heiko %D 2021 %J Health Informatics - An International Journal (HIIJ) %K Alzheimer's Diagnostic Impairment Machine Speech dementia learning support %N 01 %P 5-23 %R 10.5121/hiij.2021.10102 %T LANGUAGE CHARACTERISTICS SUPPORTING EARLY ALZHEIMER'S DIAGNOSIS THROUGH MACHINE LEARNING – A LITERATURE REVIEW %U https://aircconline.com/hiij/V10N1/10121hiij02.pdf %V 10 %X Alzheimer's dementia (AD) is the most common incurable neurodegenerative disease worldwide. Apart from memory loss, AD leads to speech disorders. Timely diagnosis is crucial to halt the progression of the disease. However, current diagnostic procedures are costly, invasive, and distressing. Early-stage AD manifests itself in speech disorders, which implies examining those. Machine Learning (ML) represents a promising instrument in this context. Nevertheless, no genuine consensus on the language characteristics to be analyzed exists. To counteract this deficit and provide topic-related researchers with a better basis for decision-making, we present, based on a literature review, favourable speech characteristics for the appliance toward AD detection via ML. Research trends to apply spontaneous speech, gained from image descriptions, as analysis basis, and points out that the combined use of acoustic, linguistic, and demographic features positively influences recognition accuracy. In total, we have identified 97 overarching acoustic, linguistic and demographic features.

@article{noauthororeditor, abstract = {Alzheimer's dementia (AD) is the most common incurable neurodegenerative disease worldwide. Apart from memory loss, AD leads to speech disorders. Timely diagnosis is crucial to halt the progression of the disease. However, current diagnostic procedures are costly, invasive, and distressing. Early-stage AD manifests itself in speech disorders, which implies examining those. Machine Learning (ML) represents a promising instrument in this context. Nevertheless, no genuine consensus on the language characteristics to be analyzed exists. To counteract this deficit and provide topic-related researchers with a better basis for decision-making, we present, based on a literature review, favourable speech characteristics for the appliance toward AD detection via ML. Research trends to apply spontaneous speech, gained from image descriptions, as analysis basis, and points out that the combined use of acoustic, linguistic, and demographic features positively influences recognition accuracy. In total, we have identified 97 overarching acoustic, linguistic and demographic features.}, added-at = {2021-03-09T07:25:12.000+0100}, author = {Thaler, Fabian and Gewald, Heiko}, biburl = {https://www.bibsonomy.org/bibtex/2513ca9b7fbab97db3d1a57f78ceff96a/jeffrenna}, doi = {10.5121/hiij.2021.10102}, interhash = {e92154a9fcb8174e1595153fe87f8ba3}, intrahash = {513ca9b7fbab97db3d1a57f78ceff96a}, issn = {ISSN 0974 - 9322 (Online); 0975 - 2293 (Print)}, journal = {Health Informatics - An International Journal (HIIJ)}, keywords = {Alzheimer's Diagnostic Impairment Machine Speech dementia learning support}, language = {english}, month = {February}, number = 01, pages = {5-23}, timestamp = {2021-03-09T07:25:12.000+0100}, title = {LANGUAGE CHARACTERISTICS SUPPORTING EARLY ALZHEIMER'S DIAGNOSIS THROUGH MACHINE LEARNING – A LITERATURE REVIEW}, url = {https://aircconline.com/hiij/V10N1/10121hiij02.pdf}, volume = 10, year = 2021 }