  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.
