LoRA-Tuned Large Language Models for Dementia Detection via Multi-View Speech-Derived Features
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a novel structured multi-view prompting framework for dementia detection that effectively integrates heterogeneous speech features into a single LLM, achieving state-of-the-art performance on the ADReSSo benchmark. While the methodological innovation in feature unification is strong, the reliance on undefined future models for key feature extraction steps and the lack of multilingual validation limit its immediate technical impact and reproducibility.
Methodology
The paper proposes a unified framework for dementia detection by integrating four distinct speech-derived feature views (lexical, temporal, discourse, phonological) into a structured JSON prompt for a LoRA-adapted Large Language Model (LLM). The core methodological contribution is the "structured multi-view reasoning" approach, which avoids traditional late-fusion or separate encoder pipelines. The feature extraction pipeline is robust: it uses Whisper for transcripts, MFA for temporal alignment/pauses, a custom LLM-based pipeline for discourse clustering, and HuPER for phonological sequences. The novelty lies in the prompt engineering strategy that allows an LLM to implicitly fuse these heterogeneous signals. However, the use of GPT-5.2 (a non-existent/future model as of current knowledge, likely a placeholder or typo for GPT-4/4o) for discourse annotation introduces a significant methodological opacity and potential data leakage or dependency issue. The reliance on external API-based models for feature extraction limits the self-containment of the proposed method.
Results & evaluation
The evaluation is conducted on the ADReSSo dataset, a standard benchmark for speech-based dementia detection. The reported F1-score of 90.14% is competitive and reportedly surpasses prior state-of-the-art systems like Swin-BERT. The ablation study effectively demonstrates the incremental contribution of each view, with discourse cues providing the largest gain. The analysis of model scaling (4B to 14B) adds value by showing that the framework is effective across different capacities. However, the comparison is limited to the ADReSSo dataset, and the results are on the test set provided by the challenge, which may have specific splits not fully detailed in the text (though standard ADReSSo splits are implied). The lack of cross-lingual evaluation is a noted limitation.
Reproducibility
Reproducibility is partially hindered by the use of "GPT-5.2" for discourse feature extraction. Unless the specific prompt and model version are strictly defined and the model is publicly available (which GPT-5.2 is not, as it does not exist yet), this step cannot be exactly reproduced. The code repository URL is provided, which is a positive step. The use of standard tools (Whisper, MFA, HuPER) aids reproducibility for those parts. The specific LoRA hyperparameters are mentioned (AdamW, LR 1e-4), but details on rank, alpha, and target modules are sparse in the abstract/summary provided.
Limitations
The paper explicitly acknowledges limitations regarding the use of commercial APIs for discourse extraction and the lack of multilingual evaluation. Additionally, the reliance on a non-existent or misnamed model (GPT-5.2) for the core feature extraction step is a major technical flaw in the description, raising questions about the validity and reproducibility of the discourse features. The "future venue" (INTERSPEECH 2026) suggests this might be a pre-print or accepted paper for a future conference, which is unusual but noted.
Broader impact
This work contributes to the field of AI for healthcare, specifically early diagnosis of neurodegenerative diseases. By providing a non-invasive, speech-based screening tool, it has significant potential for scalable, low-cost dementia screening. The unified LLM-based approach could inspire similar multi-modal reasoning frameworks in other clinical domains. However, the ethical implications of using AI for medical diagnosis, including bias and interpretability, are not deeply discussed, though the structured prompt offers some interpretability compared to black-box fusion methods. The paper presents a novel structured multi-view prompting framework for dementia detection that effectively integrates heterogeneous speech features into a single LLM, achieving state-of-the-art performance on the ADReSSo benchmark. While the methodological innovation in feature unification is strong, the reliance on undefined future models for key feature extraction steps and the lack of multilingual validation limit its immediate technical impact and reproducibility.
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