An automated speech analysis system for the detection of cognitive decline in elderly

Abstract

The goal of this study is to develop and test an automated integrated speech analysis system for detecting mild cognitive impairment (MCI) and dementia in spontaneous free speech. During the years 2010–2016, speech recordings (N = 2800) were obtained from 200 Greek Cypriots over the age of 65. These were divided into three groups (G1, G2, and G3) based on the results of their Mini-mental state examination (MMSE): G1:95 normal (NOR) individuals with an MMSE greater than 26; G2:65 MCI subjects with 20 ≤ MMSE ≤ 26; G3:40 dementia subjects with 0 ≤ MMSE < 20. As a result, each speech recording was analyzed for 55 different speech features. The features that could statistically significantly distinguish between the three aforementioned groups were selected using statistical and model multi-classification analysis. Learning-based classifiers were built using the selected features alone or in combination. For each group, statistically significant differences in speech features were detected, which may be used to differentiate the three groups. An overall multi-classification area under the curve (AUC) of 0.92 was attained using only the features identified plus clinical factors. Speech features were extracted, and they were able to discriminate people from the three groups. This study paves the way for the development of an integrated system that uses automatic speech analysis to detect early and progressive signs of cognitive decline (CD) in free speech. In a future study, the proposed method will be developed and integrated into a mobile device.