Mesterséges Intelligencia Nemzeti Laboratórium / Artificial Intelligence National
Laboratory(MILAB) Támogató: NKFIH
Enhancement of deep learning based semantic representations with acoustic-prosodic
features for a...(FK-124413) Támogató: NKFIH
Ministry for Innovation and Technology, Hungary(NKFIH-1279-2/2020)
(ÚNKP-21-5-SZTE-583) Támogató: NKFIH
Bolyai ösztöndíj((BO/00653/19) Támogató: MTA Bolyai János Kutatási Ösztöndíj) Támogató:
MTA Bolyai pályázat
In this article, we seek to automatically identify Hungarian patients suffering from
mild cognitive 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.
