  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 can
be used to differentiate between normal aging and Alzheimer's disease, and
speech analysis has been found to be a cost-effective method of detecting
Alzheimer's dementia. The aim of this review is to analyze the various
algorithms used in speech-based detection and classification 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
``Alzheimer's detection'', "speech," and "natural language processing." The
ADReSS, Pitt corpus, and 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 the findings of this study, 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 may serve as a reliable biomarker for
efficiently identifying Alzheimer's disease.
