用自然语言对话提前识别痴呆，提升早期干预可能
Early Dementia Detection Using Multiple Spontaneous Speech Prompts: The PROCESS Challenge
- 设计三个神经科医生定制的口语提问任务，捕捉认知状态
- 基线模型在分类任务中F1达55.0%，回归任务RMSE为2.98
- 适合做早期痴呆筛查与语音分析研究者参考
痴呆伴随多种认知功能损伤，通常在病情显著进展后才显现，此时干预往往无效。为应对这一挑战，预测与识别自发言语中的认知衰退（PROCESS）信号处理大赛邀请研究者聚焦早期痴呆检测。我们提供了一个新的自发言语语料库，包含由神经科医生设计的三个提问任务，旨在更全面地捕捉说话者的认知状态。基线模型在分类任务中取得55.0%的F1分数，在回归任务中达到2.98的均方根误差。
原文摘要 · Abstract (English)
Dementia is associated with various cognitive impairments and typically manifests only after significant progression, making intervention at this stage often ineffective. To address this issue, the Prediction and Recognition of Cognitive Decline through Spontaneous Speech (PROCESS) Signal Processing Grand Challenge invites participants to focus on early-stage dementia detection. We provide a new spontaneous speech corpus for this challenge. This corpus includes answers from three prompts designed by neurologists to better capture the cognition of speakers. Our baseline models achieved an F1-score of 55.0% on the classification task and an RMSE of 2.98 on the regression task.
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