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Multilingual word embeddings for the assessment of narrative speech in mild cognitive impairment

Journal article
Authors Kathleen Fraser
Kristina Lundholm Fors
Dimitrios Kokkinakis
Published in Computer Speech and Language
Volume 53
Pages 121-139
ISSN 0885-2308
Publication year 2019
Published at Department of Swedish
Centre for Ageing and Health (Agecap)
Pages 121-139
Language en
Links doi.org/10.1016/j.csl.2018.07.005
https://gup.ub.gu.se/file/207540
Keywords Dementia, Machine learning, Mild cognitive impairment, Multilingual analysis, Narrative analysis, Topic modeling
Subject categories Language Technology (Computational Linguistics)

Abstract

We analyze the information content of narrative speech samples from individuals with mild cognitive impairment (MCI), in both English and Swedish, using a combination of supervised and unsupervised learning techniques. We extract information units using topic models trained on word embeddings in monolingual and multilingual spaces, and find that the multilingual approach leads to significantly better classification accuracies than training on the target language alone. In many cases, we find that augmenting the topic model training corpus with additional clinical data from a different language is more effective than training on additional monolingual data from healthy controls. Ultimately we are able to distinguish MCI speakers from healthy older adults with accuracies of up to 63% (English) and 72% (Swedish) on the basis of information content alone. We also compare our method against previous results measuring information content in Alzheimer's disease, and report an improvement over other topic-modeling approaches. Furthermore, our results support the hypothesis that subtle differences in language can be detected in narrative speech, even at the very early stages of cognitive decline, when scores on screening tools such as the Mini-Mental State Exam are still in the “normal” range.

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