用语音特征补全失分项，提升痴呆筛查准确率
Mitigating Scoring Errors and Compensating for Nonverbal Subtests in Speech-Based Dementia Assessment
- 融合语音转录与Whisper嵌入，降低评分误差
- 仅凭语音数据即可逼近专家综合评估结果
- 适合语音辅助诊断与资源匮乏地区使用
早期认知障碍检测依赖神经心理测试以减少主观性，覆盖多个认知领域。语音评估可辅助诊断并提升可及性，但转录错误和非语言子测试（如运动技能）缺失限制了准确性。除传统测试分数外，语音衍生特征能提供额外认知状态信息。本研究针对德语标准化痴呆筛查工具Syndrom-Kurz-Test（含语言与运动子测试），训练模型融合各语言子测试的转录得分与Whisper嵌入，以降低评分误差；进而利用融合表征估算专家总体评分，补偿缺失的运动子测试。尽管省略子测试，模型仍与专家评分高度相关，并有效准确区分不同认知状态群体。
原文摘要 · Abstract (English)
Early detection of cognitive impairment relies on neuropsychological tests to minimize subjectivity by assessing multiple cognitive domains. Speech-based evaluation can support diagnostics and improve accessibility, but transcription errors and the omission of nonverbal subtests (e.g., motor skills) limit accuracy. Beyond conventional test scores, speech-derived features can provide additional insights into cognitive status. This study investigates the speech-based evaluation of the German "Syndrom-Kurz-Test," a standardized dementia screening test comprising verbal and motor subtests. We train models that integrate transcript-derived scores and Whisper embeddings per verbal subtest to reduce scoring errors. To compensate for missing motor subtests, we then leverage these fused representations to approximate expert overall ratings. Despite omitting subtests, our models strongly correlate with expert ratings and efficiently and accurately discriminate between cognitive status groups.
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。