Cross-Linguistic Persona-Driven Data Synthesis for Robust Multimodal Cognitive Decline Detection

• URL: http://arxiv.org/abs/2602.07978v1
• Date: Sun, 08 Feb 2026 14:10:05 GMT
• Title: Cross-Linguistic Persona-Driven Data Synthesis for Robust Multimodal Cognitive Decline Detection
• Authors: Rui Feng, Zhiyao Luo, Liuyu Wu, Wei Wang, Yuting Song, Yong Liu, Kok Pin Ng, Jianqing Li, Xingyao Wang,
• Abstract summary: We introduce SynCog, a novel framework integrating controllable zero-shot multimodal data synthesis with Chain-of-Thought deduction fine-tuning.<n>This generative paradigm enables the rapid, zero-shot expansion of clinical corpora across diverse languages.<n>Experiments on the ADReSS and ADReSSo benchmarks demonstrate that augmenting limited clinical data with synthetic phenotypes yields competitive diagnostic performance.
• Score: 20.599682298329213
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speech-based digital biomarkers represent a scalable, non-invasive frontier for the early identification of Mild Cognitive Impairment (MCI). However, the development of robust diagnostic models remains impeded by acute clinical data scarcity and a lack of interpretable reasoning. Current solutions frequently struggle with cross-lingual generalization and fail to provide the transparent rationales essential for clinical trust. To address these barriers, we introduce SynCog, a novel framework integrating controllable zero-shot multimodal data synthesis with Chain-of-Thought (CoT) deduction fine-tuning. Specifically, SynCog simulates diverse virtual subjects with varying cognitive profiles to effectively alleviate clinical data scarcity. This generative paradigm enables the rapid, zero-shot expansion of clinical corpora across diverse languages, effectively bypassing data bottlenecks in low-resource settings and bolstering the diagnostic performance of Multimodal Large Language Models (MLLMs). Leveraging this synthesized dataset, we fine-tune a foundational multimodal backbone using a CoT deduction strategy, empowering the model to explicitly articulate diagnostic thought processes rather than relying on black-box predictions. Extensive experiments on the ADReSS and ADReSSo benchmarks demonstrate that augmenting limited clinical data with synthetic phenotypes yields competitive diagnostic performance, achieving Macro-F1 scores of 80.67% and 78.46%, respectively, outperforming current baseline models. Furthermore, evaluation on an independent real-world Mandarin cohort (CIR-E) demonstrates robust cross-linguistic generalization, attaining a Macro-F1 of 48.71%. These findings constitute a critical step toward providing clinically trustworthy and linguistically inclusive cognitive assessment tools for global healthcare.

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