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Alzheimer’s Disease and Speech Background

  • Walker H. LandJr.
  • J. David Schaffer
Chapter
  • 384 Downloads

Abstract

Developing a possible diagnostic test for Alzheimer’s disease (AD) based on speech is used throughout this book to illustrate the application of the machine learning methods we describe. This application has many of the characteristics typical of such tasks: one has some idea that a set of features characterizing samples that one possesses might be able to classify the objects into two or more classes. Lacking sufficient depth of understanding of how this might be caused, one goes searching for useful patterns in the data—a fishing expedition.

This chapter provides background material on AD for the reader interested in how it is defined, what is known about its underlying pathology, how it is usually diagnosed in the clinic, and some of its known effects on speech. We then quickly summarize previous attempts at this task, older ones doing linguistic operations largely by hand, and more current attempts using computers. We then provide details of the speech samples we have collected, and how an array of speech features was extracted from them fully automatically, including speech to text with punctuation. Brief comments are included on issues related to experimental design.

Keywords

Alzheimer’s disease Effects on speech Diagnosis Demographics Mini-mental state exam (MMSE) Pause measurement Automatic speech-to-text Computational linguistics 

Abbreviations

ABeta

Amyloid beta protein fragment

ACADIE

Atlantic Canada Alzheimer’s Disease Investigation of Expectations

AD

Alzheimer’s disease

ADAS-Cog

Alzheimer’s Disease Assessment Scale-Cognitive

APOE4

Apolipoprotein allele variant e4

ASR

Automatic speech recognition

BDAE

Boston Diagnostic Aphasia Examination

BN

Bayesian network

CSF

Cerebrospinal fluid

FTD

Frontotemporal dementia

kDa

Kilo daltons

LOO

Leave one out cross-validation

MMSE

Mini Mental State Exam

MRI

Magnetic resonance imaging

NL

Normal control subjects

PET

Positron emission tomography

POS

Part of speech

SUNY

State University of New York

TDP-43

Transactive response DNA binding protein 43 kDa

TTR

Type token ratio

US

United States

WAB

Western Aphasia Battery

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Walker H. LandJr.
    • 1
  • J. David Schaffer
    • 2
  1. 1.Binghamton UniversityBowieUSA
  2. 2.Binghamton UniversityBinghamtonUSA

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