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Register a new userMultilingual Learning for Mild Cognitive Impairment Screening from a Clinical Speech Task
التعلم المتعدد اللغات لفحص ضعف المعرفي المعتدل من مهمة الكلام السريرية
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تعد مهمة الطلاقة اللفظية الدلالية (SVF) أداة للفحص الفعال والغازي في الكلام من أجل ضعف المعرفي المعتدل (MCI). في SVF، يتعين على الشاشات إنتاج أكبر عدد ممكن من الكلمات للحصول على فئة دلالية معينة قدر الإمكان في غضون 60 ثانية. من بين النهج الحديثة للتقييم التلقائي ل SVF توظف Word Adgeddings لتحليل أوجه التشابه الدلالي في تسلسل الكلمات هذه. في حين أثبتت هذه الأساليب الواعدة في مجموعة متنوعة من لغات الاختبار، فإن كمية البيانات الصغيرة المتاحة لأي لغة معينة تحد من الأداء. في هذه الورقة، نحن في المرة الأولى التي تحقق فيها مناهج التعلم متعددة اللغات لتصنيف MCI من SVF من أجل مكافحة ندرة البيانات. للسماح للتعميم عبر اللغات، تعتمد هذه الأساليب إما على الترجمة إلى لغة مشتركة، أو الاستفادة من العديد من تضمين كلمة مميزة. في التقييمات في جثة متعددة اللغات من المشاركين الفرنسيين الأكبر سنا والمشاركين الهولنديين والألمانيين (الضوابط = 66، MCI = 66)، نظين أن نهجنا متعددة اللغات تتحسن بوضوح على خطوط خطوط خطوط خطوط وطنية واحدة.
The Semantic Verbal Fluency Task (SVF) is an efficient and minimally invasive speech-based 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. State-of-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.
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