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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. 25679v1 Announce Type: cross Abstract: Multimodal behavioral analysis offers a scalable approach to assessing depression, anxiety, and stress, yet generic fusion models often ignore the psychometric structure of questionnaire labels.
arXiv:2608. 03611v1 Announce Type: new Abstract: Multimodal Sentiment Analysis (MSA) integrates text, audio, and vision to infer human affect, yet real-world multimodal observations are often incomplete.
arXiv:2501. 16106v2 Announce Type: replace Abstract: Recent advances in multimodal depression recognition for clinical interviews (MDRC) have demonstrated the potential of AI systems by integrating textual, acoustic, and facial cues.
arXiv:2609.14764v1 Announce Type: cross Abstract: Early detection of cognitive decline from speech offers a scalable and non-invasive alternative to conventional clinical assessment. Verbal fluency t...
arXiv:2607. 21496v1 Announce Type: cross Abstract: Cognitive impairment (CI) is a growing public health concern.