Alzheimer’s Disease and Speech Background

  • Walker H. LandJr.
  • J. David Schaffer


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.


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



Amyloid beta protein fragment


Atlantic Canada Alzheimer’s Disease Investigation of Expectations


Alzheimer’s disease


Alzheimer’s Disease Assessment Scale-Cognitive


Apolipoprotein allele variant e4


Automatic speech recognition


Boston Diagnostic Aphasia Examination


Bayesian network


Cerebrospinal fluid


Frontotemporal dementia


Kilo daltons


Leave one out cross-validation


Mini Mental State Exam


Magnetic resonance imaging


Normal control subjects


Positron emission tomography


Part of speech


State University of New York


Transactive response DNA binding protein 43 kDa


Type token ratio


United States


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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