arXiv Machine Learning

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.

arXiv AI

Jun 16

Unifying Acoustic Features and Text with Multimodal LLMs for Neurodegenerative Screening

arXiv:2606. 14788v1 Announce Type: cross Abstract: Voice-based screening offers a scalable and non-invasive way to assess neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD), but their staging remains challenging due to the difficulty of integrating heterogeneous data.

By Qingfeng Zhang, Yuanxiong Guo, Yanmin Gong

llms rag multimodal

arXiv AI

Jul 21

A Benchmark for Early-stage Parkinson's Disease Detection from Speech

arXiv:2605. 14066v2 Announce Type: replace-cross Abstract: Early-stage Parkinson's disease (EarlyPD) detection from speech is clinically meaningful yet underexplored, and published results are hard to compare because studies differ in datasets, languages, tasks, evaluation protocols, and EarlyPD definitions.

By Terry Yi Zhong, Cristian Tejedor-Garcia, Khiet P. Truong, Janna Maas, Louis ten Bosch, Bastiaan R. Bloem

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 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

Aug 11

Label-Free Parkinson's Disease Screening from Face and Voice through Mechanistic Interpretability

arXiv:2608. 08976v1 Announce Type: new Abstract: Parkinson's disease (PD) is the second most common neurodegenerative disorder.

By Jiaheng Su, Yu Sun

llms rag computer-vision benchmarks safety

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 17

SpeechDx: A Multi-Task Benchmark for Clinical Speech AI

arXiv:2606. 17339v1 Announce Type: new Abstract: Speech offers a uniquely informative window into health by simultaneously engaging neurological, motor, respiratory, and vocal systems.

By Sejal Bhalla, Larry Kieu, Aina Merchant, Eyal de Lara, Alex Mariakakis

benchmarks

arXiv AI

Jul 28

Multimodal Domain Generalization for Depression Detection: An Attention-Based BiLSTM Network with Domain-Adversarial Training

arXiv:2607. 22794v1 Announce Type: cross Abstract: Automatic depression detection with deep learning has shown promise but often suffers from limited generalization due to domain shift arising from inter-speaker variability.

By Ali Tabaraei, Federico Simonetta, Stavros Ntalampiras

multimodal benchmarks safety

arXiv AI

Jul 1

A swap-adversarial framework for improving domain generalization in electrocorticography-based Parkinson's disease classification

arXiv:2602. 10528v2 Announce Type: replace-cross Abstract: We propose a novel swap-adversarial framework that mitigates high inter-subject variability and the high-dimensional low-sample-size problem in electrocorticography (ECoG) data.

By Seongwon Jin, Hanseul Choi, Sunggu Yang, Sungho Park, Jibum Kim

benchmarks safety

arXiv AI

Jun 11

MA-DLE: Speech-based Automatic Depression Level Estimation via Memory Augmentation

arXiv:2606. 11197v1 Announce Type: cross Abstract: Speech-based automatic estimation of depression levels is essential for enabling early detection and timely intervention, particularly in resource-constrained mental health settings.

By Xuzhi Wang, Xinran Wu, Ziping Zhao, Jianhua Tao, Bj\"orn W. Schuller

benchmarks

arXiv AI

Aug 7

Is Self-Pretraining really useful to improve diagnosis in medical Time Series?

arXiv:2608. 06122v1 Announce Type: cross Abstract: Inspired by recent evidence that transformer architectures benefit from Self-PreTraining (SPT) on long-context benchmarks, we investigate whether similar gains extend to multimodal, multivariate, and even simple univariate medical time series.

By Omar Coser, Antonio Orvieto, Paolo Soda, Loredana Zollo

llms robotics multimodal benchmarks

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