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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.03525 |
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| _version_ | 1866918468617502720 |
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| author | Taherinezhad, Fatemeh Nezhad, Mohamad Javad Momeni Karimi, Sepehr Rashidi, Sina Zolnour, Ali Dadkhah, Maryam Haghbin, Yasaman AzadMaleki, Hossein Zolnoori, Maryam |
| author_facet | Taherinezhad, Fatemeh Nezhad, Mohamad Javad Momeni Karimi, Sepehr Rashidi, Sina Zolnour, Ali Dadkhah, Maryam Haghbin, Yasaman AzadMaleki, Hossein Zolnoori, Maryam |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03525 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Speech-Based Cognitive Screening: A Systematic Evaluation of LLM Adaptation Strategies Taherinezhad, Fatemeh Nezhad, Mohamad Javad Momeni Karimi, Sepehr Rashidi, Sina Zolnour, Ali Dadkhah, Maryam Haghbin, Yasaman AzadMaleki, Hossein Zolnoori, Maryam Computation and Language Artificial Intelligence Audio and Speech Processing 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. |
| title | Speech-Based Cognitive Screening: A Systematic Evaluation of LLM Adaptation Strategies |
| topic | Computation and Language Artificial Intelligence Audio and Speech Processing |
| url | https://arxiv.org/abs/2509.03525 |