Open AccessAutomatic speech analysis for the assessment of patients with predementia and Alzheimer's disease
Author(s) -
König Alexandra,
Satt Aharon,
Sorin Alexander,
Hoory Ron,
ToledoRonen Orith,
Derreumaux Alexandre,
Manera Valeria,
Verhey Frans,
Aalten Pauline,
Robert Phillipe H.,
David Renaud
Publication year - 2015
Publication title -
alzheimer's and dementia: diagnosis, assessment and disease monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.497
H-Index - 37
ISSN - 2352-8729
DOI - 10.1016/j.dadm.2014.11.012
Subject(s) - audiology , cognitive impairment , cognition , alzheimer's disease , speech recognition , disease , cognitive decline , medicine , computer science , dementia , psychology , neuroscience
Background To evaluate the interest of using automatic speech analyses for the assessment of mild cognitive impairment (MCI) and early‐stage Alzheimer's disease (AD). Methods Healthy elderly control (HC) subjects and patients with MCI or AD were recorded while performing several short cognitive vocal tasks. The voice recordings were processed, and the first vocal markers were extracted using speech signal processing techniques. Second, the vocal markers were tested to assess their “power” to distinguish among HC, MCI, and AD. The second step included training automatic classifiers for detecting MCI and AD, using machine learning methods and testing the detection accuracy. Results The classification accuracy of automatic audio analyses were as follows: between HCs and those with MCI, 79% ± 5%; between HCs and those with AD, 87% ± 3%; and between those with MCI and those with AD, 80% ± 5%, demonstrating its assessment utility. Conclusion Automatic speech analyses could be an additional objective assessment tool for elderly with cognitive decline.