  We report on the curation of several publicly available datasets for age and
gender prediction. Furthermore, we present experiments to predict age and
gender with models based on a pre-trained wav2vec 2.0. Depending on the
dataset, we achieve an MAE between 7.1 years and 10.8 years for age, and at
least 91.1% ACC for gender (female, male, child). Compared to a modelling
approach built on handcrafted features, our proposed system shows an
improvement of 9% UAR for age and 4% UAR for gender. To make our findings
reproducible, we release the best performing model to the community as well as
the sample lists of the data splits.
