LoRA-Tuned Large Language Models for Dementia Detection via Multi-View Speech-Derived Features

• URL: http://arxiv.org/abs/2606.28445v1
• Date: Fri, 26 Jun 2026 08:29:31 GMT
• Title: LoRA-Tuned Large Language Models for Dementia Detection via Multi-View Speech-Derived Features
• Abstract summary: We propose a low-rank adaptation (LoRA)-tuned large language model (LLM) that performs structured multi-view reasoning over four complementary speech-derived signals.<n>On ADReSSo, our best model achieves an F1-score of 90.14%, and ablation confirms the complementary contribution of each view.
• Score: 3.4155322317700585
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Early detection of dementia enables timely intervention, and reflecting cognitive impairment, spontaneous speech offers a non-invasive screening modality. Conventional approaches often focus on a single representational dimension -- such as acoustic descriptors, pause modeling, automatic speech recognition (ASR) transcripts, or multimodal fusion -- limiting integrative reasoning across heterogeneous cognitive symptoms. We propose a low-rank adaptation (LoRA)-tuned large language model (LLM) that performs structured multi-view reasoning over four complementary speech-derived signals: ASR transcripts with pause markers, discourse-level topic cues, temporal fluency statistics, and phonological sequences. These cues are encoded within a unified prompt, enabling a single LLM to learn a coherent decision function without modality-specific encoders or late-stage fusion. On ADReSSo, our best model achieves an F1-score of 90.14%, and ablation confirms the complementary contribution of each view.

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