标题： 解决语音中的认知障碍检测：向PROCESS挑战提交 显示英文标题

标题： Tackling Cognitive Impairment Detection from Speech: A submission to the PROCESS Challenge

摘要： 本工作描述了我们团队参加2024年PROCESS挑战赛的提交内容，旨在通过自发性语言评估认知衰退，并使用三个引导性的临床任务。这项联合努力采取了一种整体方法，包括基于知识的声学特征集和基于文本的特征集，以及基于大型语言模型的宏观语言描述符、基于停顿的声学生物标志物和多种神经表示（例如，LongFormer、ECAPA-TDNN和Trillson嵌入）。将这些特征集与不同的分类器结合，产生了一个大的模型池，从中我们选择了那些在训练、开发和单个类别性能之间提供了最佳平衡的模型。我们的结果显示，我们表现最好的系统对应于相互补充的模型组合，依赖于来自所有三个临床任务的声学和文本信息。 显示英文摘要

摘要： This work describes our group's submission to the PROCESS Challenge 2024, with the goal of assessing cognitive decline through spontaneous speech, using three guided clinical tasks. This joint effort followed a holistic approach, encompassing both knowledge-based acoustic and text-based feature sets, as well as LLM-based macrolinguistic descriptors, pause-based acoustic biomarkers, and multiple neural representations (e.g., LongFormer, ECAPA-TDNN, and Trillson embeddings). Combining these feature sets with different classifiers resulted in a large pool of models, from which we selected those that provided the best balance between train, development, and individual class performance. Our results show that our best performing systems correspond to combinations of models that are complementary to each other, relying on acoustic and textual information from all three clinical tasks.