Speech-Based Cognitive Screening: A Systematic Evaluation of LLM Adaptation Strategies

• URL: http://arxiv.org/abs/2509.03525v2
• Date: Tue, 07 Oct 2025 06:46:04 GMT
• Title: Speech-Based Cognitive Screening: A Systematic Evaluation of LLM Adaptation Strategies
• Authors: Fatemeh Taherinezhad, Mohamad Javad Momeni Nezhad, Sepehr Karimi, Sina Rashidi, Ali Zolnour, Maryam Dadkhah, Yasaman Haghbin, Hossein AzadMaleki, Maryam Zolnoori,
• Abstract summary: Over half of US adults with Alzheimer disease and related dementias remain undiagnosed.<n>We compared large language model adaptation strategies for dementia detection using the DementiaBank speech corpus.
• Score: 1.6807214708043468
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Over half of US adults with Alzheimer disease and related dementias remain undiagnosed, and speech-based screening offers a scalable detection approach. We compared large language model adaptation strategies for dementia detection using the DementiaBank speech corpus, evaluating nine text-only models and three multimodal audio-text models on recordings from DementiaBank speech corpus. Adaptations included in-context learning with different demonstration selection policies, reasoning-augmented prompting, parameter-efficient fine-tuning, and multimodal integration. Results showed that class-centroid demonstrations achieved the highest in-context learning performance, reasoning improved smaller models, and token-level fine-tuning generally produced the best scores. Adding a classification head substantially improved underperforming models. Among multimodal models, fine-tuned audio-text systems performed well but did not surpass the top text-only models. These findings highlight that model adaptation strategies, including demonstration selection, reasoning design, and tuning method, critically influence speech-based dementia detection, and that properly adapted open-weight models can match or exceed commercial systems.

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