Alzheimer's disease and automatic speech analysis: A review

Abstract

The objective of this paper is to present the state of-the-art relating to automatic speech and voice analysis techniques as applied to the monitoring of patients suffering from Alzheimer's disease as well as to shed light on possible future research topics. This work reviews more than 90 papers in the existing literature and focuses on the main feature extraction techniques and classification methods used. In order to guide researchers interested in working in this area, the most frequently used data repositories are also given. Likewise, it identifies the most clinically relevant results and the current lines developed in the field. Automatic speech analysis, within the Health 4.0 framework, offers the possibility of assessing these patients, without the need for a specific infrastructure, by means of non-invasive, fast and inexpensive techniques as a complement to the current diagnostic methods.

Ten Years of Research on Automatic Voice and Speech Analysis of People With Alzheimer's Disease and Mild Cognitive Impairment: A Systematic Review Article

“…However, they focus on other neurodegenerative processes (Boschi et al, 2017 ), conduct a general analysis of speech elicited by pictures in AD (Mueller et al, 2018 ; Slegers et al, 2018 ), or make a comprehensive but unsystematic review (Szatloczki et al, 2015 ). Shortly before the submission of this paper, a review was published by Pulido et al ( 2020 ) in which they make an excellent contribution by providing an in-depth analysis of the multiple methods and databases commonly used in the field. Nevertheless, we believe that the one we present provides added value by contributing the risk of bias analysis, as it is essential to assess the validity of the evidence found.…”

Evaluation of Speech-Based Digital Biomarkers: Review and Recommendations

“…A growing body of work is highlighting the ongoing clinical validation of speech-based measures in a variety of clinical contexts. Speech has been demonstrated to have diagnostic validity for Alzheimer's disease (AD) and mild cognitive impairment (MCI) in studies using machine-learning classification models to differentiate individuals with AD/MCI from healthy individuals based on speech samples [34][35][36][37][38][39][40][41]. Additionally, speech analysis has been shown to be able to detect individuals with depression [42][43][44][45], schizophrenia [46][47][48][49], autism spectrum disorder [50], and Parkinson's disease [51,52], and can differentiate the subtypes of primary progressive aphasia and frontotemporal dementia [53][54][55].…”

Longitudinal Speech Biomarkers for Automated Alzheimer's Detection

“…Previous work, instead, has very little inspiration on the different stages of human intelligence or focuses solely on modelling a small part of the brain [2,3]. The OVBM framework can be used for various tasks such as speech segmentation and transcription -in this paper we demonstrate it in the individualized and explainable diagnostic of Alzheimer's Dementia (AD) patients, where, as shown in Table 1 we achieve above state-of-the-art accuracy of 93.8% [4] and using only raw audio as input, while extracting, for each subject a saliency map with the relative disease progression of 16 biomarkers. Even with expensive CT scans, to date experts can not create consistent biomarkers [5,6,7] even when including emotional biomarkers, unlike our approach which automatically develops them from free speech.…”