The paper examines why speech‑based screening for Alzheimer’s disease fails to generalize across different languages, tasks, and recording protocols. Using a leave‑one‑corpus‑out evaluation on four datasets, it finds that 59 of 70 interpretable speech features show conflicting patterns between healthy controls and cognitive risk groups, with pause, silence, and speech rate being highly protocol‑sensitive. The authors propose a fusion method that combines XLM‑R text baseline scores with evidence anchors, improving mean speaker AUC to 0.785 and worst‑case AUC to 0.615, and emphasize the importance of auditing feature transferability and reporting worst‑case domain robustness.
By Zijian Lu, Sizhe Liu, Yin Zhang, Jixuan Deng, Xinrong Lin, Xinchen Yuan, Chicheng Jin, Yiping Zuo, Yuanchao Li
The study examines how speech preprocessing—such as enhancement, sample selection, and demographic balancing—affects Alzheimer’s disease detection models that use the Pitt Corpus. Experiments reveal that while speech‑enhanced datasets boost in‑domain accuracy, they diminish cross‑dataset robustness and introduce class imbalance and prediction shifts, even when training and testing enhancements are matched. Large audio‑language models show similar sensitivity, indicating that cleaner speech does not guarantee better real‑world performance.
By Luqi Sun, Shreeram Suresh Chandra, Lin Zhang, You-Jin Li, Brian MacWhinney, Yu Tsao, Emily Mower Provost, Berrak Sisman
Speech-based Alzheimer's disease (AD) detection increasingly relies on speech-enhanced and curated versions of the Pitt Corpus, where speech enhancement, sample selection, and demographic balancing ar...
arXiv:2602. 11177v2 Announce Type: replace-cross Abstract: Reliable early detection of Alzheimer's disease (AD) is challenging, particularly due to the limited availability of labeled data.
By Lei Jiang, Yue Zhou, Natalie Parde
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
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
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
arXiv:2608. 07378v1 Announce Type: cross Abstract: Early diagnosis of Alzheimer's disease (AD) is critical for enabling timely interventions that may slow disease progression and improve patient outcomes.
By Xin Wang, Yingchao Huang, Yuhan Su, Shanshan Yao, Wei Peng
arXiv:2602.13047v2 Announce Type: replace
Abstract: Conversational speech reveals early signs of cognitive decline, including dementia and mild cognitive impairment (MCI). AI models show promise for...
By Madhurananda Pahar, Caitlin Illingworth, Dorota Braun, Bahman Mirheidari, Lise Sproson, Daniel Blackburn, Heidi Christensen
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
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
The paper evaluates pre‑trained speech embeddings from four state‑of‑the‑art speech foundation models for cross‑lingual Parkinson's disease severity assessment. Experiments span three datasets in zero‑shot and k‑shot settings, showing that these embeddings can transfer meaningfully across languages, though performance varies with dataset characteristics, preprocessing, and adaptation strategy. Misclassifications linked to inter‑speaker variability and atypical speech patterns underscore the need for more robust feature extraction, modeling, and explainability to support reliable clinical insights.
By Simon Pals, Cristian Tejedor-Garcia