Abstract
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.
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References
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):2081–2090
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 12:29–34
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 2011
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 14:71–91
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.
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), 2007
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–M626
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):528–532
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–E23
Vassiliki I, Stergios K (2003) Clinical psychoacoustics in Alzheimer’s disease central auditory processing disorders and speech deterioration. Annal Gen Hosp Psychiat 2:12
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Khodabakhsh, A., Demiroglu, C. (2015). Analysis of Speech-Based Measures for Detecting and Monitoring Alzheimer’s Disease. In: Fernández-Llatas, C., García-Gómez, J. (eds) Data Mining in Clinical Medicine. Methods in Molecular Biology, vol 1246. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1985-7_11
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DOI: https://doi.org/10.1007/978-1-4939-1985-7_11
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Publisher Name: Humana Press, New York, NY
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