Building on the success of the ADReSS Challenge at Interspeech 2020, which
attracted the participation of 34 teams from across the world, the ADReSSo
Challenge targets three difficult automatic prediction problems of societal and
medical relevance, namely: detection of Alzheimer's Dementia, inference of
cognitive testing scores, and prediction of cognitive decline. This paper
presents these prediction tasks in detail, describes the datasets used, and
reports the results of the baseline classification and regression models we
developed for each task. A combination of acoustic and linguistic features
extracted directly from audio recordings, without human intervention, yielded a
baseline accuracy of 78.87% for the AD classification task, an MMSE prediction
root mean squared (RMSE) error of 5.28, and 68.75% accuracy for the cognitive
decline prediction task.

本文介绍了 ADReSSo Challenge 的三项预测任务，分别是：阿尔茨海默病的检测，认知测试得分的推断和认知能力下降的预测。通过从音频记录中直接提取声学和语言特征，本文开发的分类和回归模型的基准准确率分别达到了 78.87％，5.28 分的 RMSE 误差和 68.75％。