We present a novel benchmark dataset and prediction tasks for investigating approaches to assess cognitive function through analysis of connected speech. The dataset consists of speech samples and clinical information for speakers of Mandarin Chinese and English with different levels of cognitive impairment as well as individuals with normal cognition. These data have been carefully matched by age and sex by propensity score analysis to ensure balance and representativity in model training. The prediction tasks encompass mild cognitive impairment diagnosis and cognitive test score prediction. This framework was designed to encourage the development of approaches to speech-based cognitive assessment which generalise across languages. We illustrate it by presenting baseline prediction models that employ language-agnostic and comparable features for diagnosis and cognitive test score prediction. The models achieved unweighted average recall was 59.2% in diagnosis, and root mean squared error of 2.89 in score prediction.

翻译：我们提出了一个新颖的基准数据集及预测任务，用于研究通过连续语音分析评估认知功能的方法。该数据集包含普通话和英语使用者的语音样本及临床信息，这些使用者具有不同程度的认知障碍，同时也包含认知正常的个体。通过倾向评分分析，这些数据在年龄和性别上进行了仔细匹配，以确保模型训练中的平衡性和代表性。预测任务涵盖轻度认知障碍诊断和认知测试分数预测。本框架旨在鼓励开发能够跨语言泛化的基于语音的认知评估方法。我们通过展示基线预测模型来说明该框架，这些模型采用与语言无关且可比较的特征进行诊断和认知测试分数预测。模型在诊断任务中的未加权平均召回率为59.2%，在分数预测中的均方根误差为2.89。