Cognitive impairment detection through spontaneous speech offers potential for early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). The PROCESS Grand Challenge, part of ICASSP 2025, focuses on advancing this field with innovative solutions for classification and regression tasks. In this work, we integrate interpretable features with temporal features extracted from pre-trained models through a multimodal fusion strategy. For the classification task, our model achieved an F1-score of 0.649 in predicting cognitive states (healthy, MCI, dementia). For the regression task, which involves MMSE score prediction, we obtained a root-mean-square error (RMSE) of 2.628. These results led to our team securing the top overall ranking in the competition.

翻译：通过自发语音检测认知障碍为阿尔茨海默病（AD）和轻度认知障碍（MCI）的早期诊断提供了潜力。作为ICASSP 2025一部分的PROCESS Grand Challenge，致力于通过创新的分类与回归任务解决方案推动该领域发展。在本研究中，我们通过多模态融合策略，将可解释性特征与从预训练模型中提取的时序特征相结合。在分类任务中，我们的模型在预测认知状态（健康、MCI、痴呆）方面取得了0.649的F1分数。在涉及MMSE分数预测的回归任务中，我们获得了2.628的均方根误差（RMSE）。这些成果使我们的团队在竞赛中获得了总排名第一的成绩。