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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.