  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.
