Publikation
Multilingual Learning for Mild Cognitive Impairment Screening from a Clinical Speech Task
Hali Lindsay; Philipp Müller; Insa Kröger; Johannes Tröger; Nicklas Linz; Alexandra König; Radia Zeghari; Frans RJ Verhey; Inez HGB Ramakers
In: INTERNATIONAL CONFERENCE RECENT ADVANCES IN NATURAL LANGUAGE PROCESSING. Recent Advances in Natural Language Processing (RANLP-2021), September 1-7, Pages 835-843, Vol. 13, INCOMA Ltd. 2021.
Zusammenfassung
The Semantic Verbal Fluency Task (SVF) is an efficient and minimally invasive speechbased screening tool for Mild Cognitive Impairment (MCI). In the SVF, testees have to produce as many words for a given semantic category as possible within 60 seconds. Stateof-the-art approaches for automatic evaluation of the SVF employ word embeddings to analyze semantic similarities in these word sequences. While these approaches have proven promising in a variety of test languages, the small amount of data available for any given language limits the performance. In this paper, we for the first time investigate multilingual learning approaches for MCI classification from the SVF in order to combat data scarcity. To allow for cross-language generalisation, these approaches either rely on translation to a shared language, or make use of several distinct word embeddings. In evaluations on a multilingual corpus of older French, Dutch, and German participants (Controls= 66, MCI= 66), we show that our multilingual approaches clearly improve over single-language baselines.