Toward Generalizable Cognitive Impairment Detection with Speech-Based Multimodal Large Language Models
Cognitive impairment (CI) is a growing public health concern. Early and accurate diagnosis is critical for enabling timely intervention and improving patient outcomes. Speech-based CI detection has emerged as a promising non-invasive approach, as speech signals encode both linguistic and acoustic markers associated with cognitive decline. Recent advances in large language models (LLMs) further strengthen the potential of speech-based assessment by enabling more expressive representation learning and improved generalization across diverse speakers, recording devices, and clinical environments.
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- PossiblePossibly related (embedding) · 45%Co-pilot, Not Autopilot: A Practical Method for Using Large Language Models in Interventional Cardiology - EMJ →
- FuzzySimilar title/name (fuzzy) · 87%huggingface/speech-to-speech →
“Fuzzy title match (0.94): “Toward Generalizable Cognitive Impairment Detection with Spe” ≈ “huggingface/speech-to-speech””
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- LinkedLinked via arxiv author · 85%Yingchao Huang →
“Toward Generalizable Cognitive Impairment Detection with Speech-Based Multimodal Large Language Models”
- LinkedLinked via arxiv author · 85%Zexin Wang →
“Toward Generalizable Cognitive Impairment Detection with Speech-Based Multimodal Large Language Models”
- LinkedLinked via arxiv author · 85%Yuhan Su →
“Toward Generalizable Cognitive Impairment Detection with Speech-Based Multimodal Large Language Models”
- LinkedLinked via arxiv author · 85%Shanshan Yao →
“Toward Generalizable Cognitive Impairment Detection with Speech-Based Multimodal Large Language Models”