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
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
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
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
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
The study investigates whether acoustic factors encoded in pretrained self‑supervised learning (SSL) models can systematically influence predictions in speech‑based Alzheimer's disease (AD) assessment. Using the ADReSSo dataset and three large SSL backbones, the authors applied controlled noise and reverberation interventions to various audio segments and combined layer‑wise decoding, input‑ and representation‑space interventions, and geometric alignment analysis. The results demonstrate that such acoustic interventions alter AD predictions across all models, with noise producing the strongest effect, and that these effects are structured relative to the classifier’s decision direction and reproducible on a held‑out test set.
By Serli Kopar, Alkis Koudounas, Roshan P. Rane, Sam Gijsen, Paula A. Perez-Toro, Kerstin Ritter