Multilingual Cognitive Impairment Detection in the Era of Foundation Models

• URL: http://arxiv.org/abs/2604.06758v1
• Date: Wed, 08 Apr 2026 07:22:43 GMT
• Title: Multilingual Cognitive Impairment Detection in the Era of Foundation Models
• Abstract summary: We evaluate cognitive impairment (CI) classification from transcripts of speech in English, Slovene, and Korean.<n>We compare zero-shot large language models (LLMs) used as direct classifiers under three input settings.
• Score: 6.141505279958125
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We evaluate cognitive impairment (CI) classification from transcripts of speech in English, Slovene, and Korean. We compare zero-shot large language models (LLMs) used as direct classifiers under three input settings -- transcript-only, linguistic-features-only, and combined -- with supervised tabular approaches trained under a leave-one-out protocol. The tabular models operate on engineered linguistic features, transcript embeddings, and early or late fusion of both modalities. Across languages, zero-shot LLMs provide competitive no-training baselines, but supervised tabular models generally perform better, particularly when engineered linguistic features are included and combined with embeddings. Few-shot experiments focusing on embeddings indicate that the value of limited supervision is language-dependent, with some languages benefiting substantially from additional labelled examples while others remain constrained without richer feature representations. Overall, the results suggest that, in small-data CI detection, structured linguistic signals and simple fusion-based classifiers remain strong and reliable signals.

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