Analysis of Speech-Based Measures for Detecting and Monitoring Alzheimer’s Disease
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 wordsAlzheimer’s disease Speech analysis Support vector machines
- 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
- 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.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