Analysis of Speech-Based Measures for Detecting and Monitoring Alzheimer’s Disease

  • A. Khodabakhsh
  • C. DemirogluEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1246)


Automatic diagnosis of the Alzheimer’s disease as well as monitoring of the diagnosed patients can make significant economic impact on societies. We investigated an automatic diagnosis approach through the use of speech based features. As opposed to standard tests, spontaneous conversations are carried and recorded with the subjects. Speech features could discriminate between healthy people and the patients with high reliability. Although the patients were in later stages of Alzheimer’s disease, results indicate the potential of speech-based automated solutions for Alzheimer’s disease diagnosis. Moreover, the data collection process employed here can be done inexpensively by call center agents in a real-life application. Thus, the investigated techniques hold the potential to significantly reduce the financial burden on governments and Alzheimer’s patients.

Key words

Alzheimer’s disease Speech analysis Support vector machines 


  1. 1.
    Roark B, Mitchell M, Hosom J, Hollingshead K, Kaye J (2011) Spoken language derived measures for detecting mild cognitive impairment. IEEE Trans Audio Speech Lang Process 19(7):2081–2090CrossRefGoogle Scholar
  2. 2.
    Hoffmann I, Nemeth D, Dye CD, Pákáski M, Irinyi T, Kálmán J (2010) Temporal parameters of spontaneous speech in Alzheimer’s disease. Int J Speech Lang Pathol 12:29–34PubMedCrossRefGoogle Scholar
  3. 3.
    Leea H, Gayraudb F, Hirsha F, Barkat-Defradas M (2011), Speech dysfluencies in normal and pathological aging: a comparison between Alzheimer patients and healthy elderly subjects, 17th International Conference on Phonetic Sciences, Hong Kong, Aug 2011Google Scholar
  4. 4.
    Bucks RS, Singh S, Cuerden JM, Wilcock GK (2000) Analysis of spontaneous, conversational speech in dementia of Alzheimer type: evaluation of an objective technique for analysing lexical performance. Aphasiology 14:71–91CrossRefGoogle Scholar
  5. 5.
    Thomas C, Cercone N (2005) Automatic detection and rating of dementia of Alzheimer type through lexical analysis of spontaneous speech. In Proc of IEEE ICMA, 2005.Google Scholar
  6. 6.
    Roark B, Hosom J, Mitchell M, Kaye J (2007) Automatically derived spoken language markers for detecting mild cognitive impairment. In Proc 2nd Int Conf Technol Aging (ICTA), 2007Google Scholar
  7. 7.
    Boise L, Neal M, Kaye J (2004) Dementia assessment in primary care: results from a study in three managed care systems. J Gerontol 59(6):M621–M626CrossRefGoogle Scholar
  8. 8.
    Snowdon D, Kemper S, Mortimer J, Greiner L, Wekstein D, Markesbery W (1996) Linguistic ability in early life and cognitive function and Alzheimer’s disease in late life. Findings from the Nun Study. J Am Med Assoc 275(7):528–532CrossRefGoogle Scholar
  9. 9.
    Tosto G, Gasparini M, Lenzi GL, Bruno G (2011) Prosodic impairment in Alzheimer’s disease: assessment and clinical relevance. J Neuropsychiatry Clin Neurosci 23:E21–E23PubMedCrossRefGoogle Scholar
  10. 10.
    Vassiliki I, Stergios K (2003) Clinical psychoacoustics in Alzheimer’s disease central auditory processing disorders and speech deterioration. Annal Gen Hosp Psychiat 2:12CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, New York 2015

Authors and Affiliations

  1. 1.Ozyegin UniversityIstanbulTurkey
  2. 2.Faculty of EngineeringOzyegin UniversityÇekmeköy, - İstanbulTurkey

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