A Review of Alzheimer’s Disease Detection from Spontaneous Speech and Text

In the past decade, there has been a surge in research examining the use of voice and speech analysis as a means of detecting neurodegenerative diseases such as Alzheimer’s. Many studies have shown that certain acoustic features are very useful in differentiating memory changes due to normal aging from Alzheimer’s disease, and speech analysis is found to be an economical way of identifying dementia. This review aims to analyze the various algorithms used in communication-based identification and grouping of Alzheimer’s disease. A literature survey was conducted using databases such as Web of Science, Google Scholar, and Science Direct, and articles published from January 2020 to the present were included based on keywords such as “speech,” "Alzheimer’s detection," and “natural language processing.” The ADReSS, Pitt corpus, and Carolinas Conversation Collection (CCC) datasets are commonly used for the analysis of dementia from speech, and this review focuses on the various acoustic and linguistic feature engineering-based classification models drawn from 15 studies.Based on this study’s findings, it appears that a more accurate model for classifying Alzheimer’s disease can be developed by considering both linguistic and acoustic data. The review suggests that speech signals can be a useful tool for detecting dementia and can be a reliable biomarker for efficiently identifying Alzheimer’s Dementia.