arXiv Machine Learning

CCMAN: Cognitive Instability-Aware Cross-Modal Attention Network for Interpretable Temporal Biomarkers of Verbal Fluency Speech

arXiv AI

Jul 1

ASR-Agnostic Multimodal Spectrotemporal Modeling for Early Dementia Detection

arXiv:2606. 30646v1 Announce Type: cross Abstract: Speech recruits the same executive, attentional, and working memory processes underlying instrumental activities of daily living, or IADLs, providing a non-invasive proxy for cognitive assessment.

By Chukwuemeka Ugwu, Oluwafemi Richard Oyeleke

llms rag multimodal benchmarks

arXiv Machine Learning

Jul 24

Toward Generalizable Cognitive Impairment Detection with Speech-Based Multimodal Large Language Models

arXiv:2607. 21496v1 Announce Type: cross Abstract: Cognitive impairment (CI) is a growing public health concern.

By Yingchao Huang, Xin Wang, Yuhan Su, Shanshan Yao

llms rag multimodal benchmarks safety

arXiv Machine Learning

Jul 28

Dementia classification from spontaneous speech using wrapper-based feature selection

arXiv:2502. 03484v3 Announce Type: replace-cross Abstract: Dementia encompasses a group of syndromes that impair cognitive functions such as memory, reasoning, and the ability to perform daily activities.

By Marko Niemel\"a, Mikaela von Bonsdorff, Sami \"Ayr\"am\"o, Tommi K\"arkk\"ainen

arXiv AI

Jun 30

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

arXiv:2606. 28445v1 Announce Type: cross Abstract: Early detection of dementia enables timely intervention, and reflecting cognitive impairment, spontaneous speech offers a non-invasive screening modality.

By Jonghyeon Park, Olivier Jiyoun Jung, Myungwoo Oh

llms fine-tuning multimodal benchmarks

arXiv AI

Jul 14

Transcript-Free Lightweight Detection of Alzheimer's Disease from Spontaneous Speech Using Handcrafted MFCC-Dominant Acoustic Biomarkers

arXiv:2607. 10168v1 Announce Type: cross Abstract: It is still hard to find Alzheimer's disease (AD) early, especially when neuroimaging is expensive or tools that depend on language are not available.

By Rashin Gholijani Farahani, Azam Bastanfard

multimodal

arXiv Machine Learning

Jun 9

Multi-View Speech Representation Learning for Parkinson's Disease Detection Using Context-guided Cross-modal Attention

arXiv:2606. 09271v1 Announce Type: cross Abstract: Parkinson's disease (PD) is a progressive neurodegenerative disorder that frequently causes speech impairments associated with hypokinetic dysarthria.

By George Theodosiou, Loukas Ilias, Dimitris Askounis

rag multimodal benchmarks

arXiv Machine Learning

Jun 18

Fair Cognitive Impairment Detection Through Unlearning

arXiv:2606. 18571v1 Announce Type: new Abstract: Mild Cognitive Impairment (MCI) is a medical condition characterized by a noticeable decline in memory, language, or thinking abilities.

By William Nguyen, Jiali Cheng, Hadi Amiri

rag multimodal benchmarks

arXiv AI

Sep 1

TAMI: Temporally Aligned, Missingness-Aware, and Interpretable Multimodal Fusion for Mental Health Assessment in Older Adults with Mild Cognitive Impairment

arXiv:2608.30857v1 Announce Type: cross Abstract: Depression and anxiety in older adults with Mild Cognitive Impairment (MCI) are frequently underdiagnosed due to limited access to care. Multimodal a...

By Merna Bibars, Bolaji Omofojoye, Allan I. Levey, Rachel Hershenberg, Gari D. Clifford, Hyeokhyen Kwon

multimodal safety

arXiv Computation and Language

Sep 11

LLM-Anchored Paralinguistic Enrichment for Alzheimer's Disease Detection

The paper introduces LLM-Anchored Paralinguistic Enrichment (LAPE), a method that enhances language model representations with speech-based paralinguistic cues such as pauses and word elongations. LAPE incorporates three innovations: prosodic event textualization, lexico-prosodic unitization and chunking, and text-anchored paralinguistic fusion using NormGate. Evaluations on the ADReSS and ADReSSo datasets show that LAPE achieves state‑of‑the‑art performance in detecting Alzheimer’s disease from speech.

By Xiao Wei, Yuqin Lin, Yaru Cao, Jinyu Li, Bin Wen, Kai Li, Yueying Chen, Longbiao Wang, Jianwu Dang

llms rag benchmarks

arXiv AI

Jul 1

Listening Between the Lines: Joint Learning of ASR Embeddings and LLM-Augmented Linguistics for Dementia Detection

arXiv:2606. 30675v1 Announce Type: cross Abstract: Early detection of dementia through speech analysis offers a non-invasive screening alternative, but capturing both acoustic and linguistic biomarkers remains challenging.

By Olivier Jiyoun Jung, Jonghyeon Park, Myungwoo Oh

llms rag multimodal benchmarks

arXiv AI

Jun 26

Language-Based Digital Twins for Elderly Cognitive Assistance

arXiv:2606. 27334v1 Announce Type: new Abstract: Digital twins have emerged as a promising paradigm for personalized healthcare, enabling modeling of individual behavior and health trajectories.

By Mohammad Mehdi Hosseini, Mohammad H. Mahoor, Hiroko H. Dodge

llms

arXiv AI

Jun 2

Brain-Atlas-Guided Generative Counterfactual Attention for Explainable Cognitive Decline Diagnosis Using Multimodal Connectomes

arXiv:2606. 01237v1 Announce Type: new Abstract: Mild cognitive impairment (MCI) and subjective cognitive decline (SCD) are closely associated with the early Alzheimer's disease continuum, where accurate and explainable diagnosis is important for early risk assessment and intervention.

By Xiongri Shen, Jiaqi Wang, Zhenxi Song, Yi Zhong, Leilei Zhao, Xin He, Baiying Lei, Zhiguo Zhang

llms multimodal benchmarks safety

Related stories