Linguistic Parameters of Spontaneous Speech for Identifying Mild Cognitive Impairment and Alzheimer Disease

Linguistic Parameters of Spontaneous Speech for Identifying Mild Cognitive Impairment and Alzheimer Disease Veronika Vincze MTA-SZTE Research Group on Artificial Intelligence vinczev@inf.u-szeged.hu Martina Katalin Szab´o MTA TK Computational Social Science - Research Center for Educational and Network Studies (CSS-RECENS) and University of Szeged Institute of Informatics martina@inf.u-szeged.hu Ildik´o Hoffmann Research Centre for Linguistics E¨otv¨os Lorand Research Network and University of Szeged Department of Hungarian Linguistics, Szeged hoffmannildi@gmail.com L´aszl´o T´oth University of Szeged Institute of Informatics tothl@inf.u-szeged.hu Magdolna P´ak´aski University of Szeged Department of Psychiatry babikne.pakaski.magdolna @med.u-szeged.hu Submission received: 9 July 2020; revised version received: 14 September 2021; accepted for publication: 5 December 2021. https://doi.org/10.1162/COLI a 00428 © 2022 Association for Computational Linguistics Published under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license Computational Linguistics Volume 48, Number 1 J´anos K´alm´an University of Szeged Department of Psychiatry kalman.janos@med.u-szeged.hu G´abor Gosztolya MTA-SZTE Research Group on Artificial Intelligence ggabor@inf.u-szeged.hu In this article, we seek to automatically identify Hungarian patients suffering from mild cog- nitive impairment (MCI) or mild Alzheimer disease (mAD) based on their speech transcripts, focusing only on linguistic features. In addition to the features examined in our earlier study, we introduce syntactic, semantic, and pragmatic features of spontaneous speech that might affect the detection of dementia. In order to ascertain the most useful features for distinguishing healthy controls, MCI patients, and mAD patients, we carry out a statistical analysis of the data and investigate the significance level of the extracted features among various speaker group pairs and for various speaking tasks. In the second part of the article, we use this rich feature set as a basis for an effective discrimination among the three speaker groups. In our machine learning experiments, we analyze the efficacy of each feature group separately. Our model that uses all the features achieves competitive scores, either with or without demographic information (3-class accuracy values: 68%–70%, 2-class accuracy values: 77.3%–80%). We also analyze how different data recording scenarios affect linguistic features and how they can be productively used when distinguishing MCI patients from healthy controls. 1. Introduction Alzheimer disease (AD) is a neurodegenerative disorder that develops for years before clinical manifestation, while mild cognitive impairment (MCI) is usually viewed as a prodromal stage of AD (Galvin and Sadowsky 2012). Symptoms such as language dysfunctions may even occur nine years before the actual diagnosis (APA 2000). Thus, the language use of the patient may suggest MCI well before the clinical diagnosis of dementia. For both types of neurodegenerative disorders, an early diagnosis is crucial in order to allow timely treatment to decelerate progression (Nelson and Tabet 2015). However, according to Boise at al., for many MCI patients (up to 50%) MCI is never recognized (Boise, Neal, and Kaye 2004). A reason for this might be that in the early stages of the disease it is not easy for experts to detect cognitive impairment. Tests that are the most sensitive to cognitive and linguistic changes occurring in early AD and other types of dementia have been intensively …

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