AI模型利用语音分析进行痴呆症检测和临床洞察 · 跟踪4个来源

报道来源 [4]

1. arXiv cs.AI TIER_1 English(EN) · Olivier Jiyoun Jung, Jonghyeon Park, Myungwoo Oh · 2026-07-01 04:00

   倾听弦外之音：ASR嵌入与LLM增强语言学的联合学习用于痴呆症检测

   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. We propose a multimodal framework leveraging Whisper for d…

2. arXiv cs.AI TIER_1 English(EN) · Jonghyeon Park, Olivier Jiyoun Jung, Myungwoo Oh · 2026-06-30 04:00

   用于通过多视图语音衍生特征检测痴呆症的 LoRA 微调大语言模型

   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. Conventional approaches often focus on a single representational dimension …

3. arXiv cs.AI TIER_1 English(EN) · Yasaman Haghbin, Sina Rashidi, Ali Zolnour, Fatemeh Taherinezhad, Ali Fartoot, Hossein Azadmaleki, James M Noble, Maryam Dadkhah, Maryam Zolnoori · 2026-06-29 04:00

   从黑箱到临床洞察：用于语音认知障碍检测的多阶段可解释框架

   arXiv:2606.27973v1 Announce Type: cross Abstract: Speech-based cognitive impairment detection offers a noninvasive, accessible alternative to costly biomarker assays, yet transformer-based models remain clinically uninterpretable. We propose a multi-stage explainability framework…

4. arXiv cs.AI TIER_1 English(EN) · Maryam Zolnoori · 2026-06-26 11:21

   从黑箱到临床洞察：用于语音认知障碍检测的多阶段可解释框架

   Speech-based cognitive impairment detection offers a noninvasive, accessible alternative to costly biomarker assays, yet transformer-based models remain clinically uninterpretable. We propose a multi-stage explainability framework that translates black-box transformer predictions…