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.
CogDrift-4D introduces a hierarchical 4D Transformer framework for detecting mild cognitive impairment (MCI) by modeling temporal behavioral decay across seven unscripted multimodal interactions over 18 months. The approach represents continuous, multi‑session data as a dense 4D tensor and employs dual‑level modeling: intra‑session least‑squares regression to capture acute fading of facial and acoustic features, and an inter‑session recursive cross‑attention encoder to accumulate cumulative fatigue. A localized Quality Gate filters low‑integrity features, and early fusion integrates semantic and raw dynamics, achieving an MCI classification accuracy of 0.733 ± 0.13 and an F1‑score of 0.716 ± 0.14, surpassing advanced baselines.
The paper introduces a contactless, multimodal screening framework that uses video, audio, and text data collected during structured voice interactions with Amazon Alexa to detect mild cognitive impairment (MCI). It evaluates three conversational tasks—spontaneous speech, comprehension, and recall—to determine which provides the most informative signals, compares unimodal models, and assesses multimodal fusion. Results show that while individual modalities perform best on specific tasks, the multimodal model consistently achieves higher accuracy (up to 0.76) across tasks, indicating that integrated behavioral cues can reliably distinguish MCI from healthy aging.
arXiv:2607. 21496v1 Announce Type: cross Abstract: Cognitive impairment (CI) is a growing public health concern.
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.
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.
The study explores acoustic biomarkers derived from spontaneous speech to differentiate Alzheimer's disease patients from healthy controls. Using a structured picture‑description task, low‑level prosodic, spectral, and temporal features were extracted and fed into various machine learning classifiers. The best models achieved clinically meaningful separation between diagnostic groups, indicating that non‑invasive audio screening could aid early detection of dementia.
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.
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.
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: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.
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...