首个阿尔茨海默病语音情绪标注数据集，揭示患者情绪表达异常
DementiaBank-Emotion: A Multi-Rater Emotion Annotation Corpus for Alzheimer's Disease Speech (Version 1.0)
- 构建108名患者语音的多标注情绪数据集，涵盖六种基本情绪
- 患者非中性情绪占比16.9%，显著高于健康人5.7%（p<0.001）
- 适合临床语音分析、情绪识别及老年认知障碍研究者使用
我们提出DementiaBank-Emotion，首个针对阿尔茨海默病（AD）语音的多标注情绪语料库。对108名说话者共1,492个语句进行埃克曼六种基本情绪和中性情绪标注，发现患者表达非中性情绪比例达16.9%，显著高于健康对照组的5.7%（p < .001）。探索性声学分析显示：对照组在表达悲伤时基频下降3.45个半音，而患者仅上升0.11个半音（交互作用p = .023），但该结果样本量有限（悲伤：对照组n=5，患者n=15），需重复验证。在患者语音中，响度可区分不同情绪类别，表明情绪与语调映射关系部分保留。我们公开发布语料库、标注指南及校准工作坊材料，支持临床人群情绪识别研究。
原文摘要 · Abstract (English)
We present DementiaBank-Emotion, the first multi-rater emotion annotation corpus for Alzheimer's disease (AD) speech. Annotating 1,492 utterances from 108 speakers for Ekman's six basic emotions and neutral, we find that AD patients express significantly more non-neutral emotions (16.9%) than healthy controls (5.7%; p < .001). Exploratory acoustic analysis suggests a possible dissociation: control speakers showed substantial F0 modulation for sadness (Delta = -3.45 semitones from baseline), whereas AD speakers showed minimal change (Delta = +0.11 semitones; interaction p = .023), though this finding is based on limited samples (sadness: n=5 control, n=15 AD) and requires replication. Within AD speech, loudness differentiates emotion categories, indicating partially preserved emotion-prosody mappings. We release the corpus, annotation guidelines, and calibration workshop materials to support research on emotion recognition in clinical populations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。