  The ICML Expressive Vocalization (ExVo) Competition is focused on
understanding and generating vocal bursts: laughs, gasps, cries, and other
non-verbal vocalizations that are central to emotional expression and
communication. ExVo 2022, includes three competition tracks using a large-scale
dataset of 59,201 vocalizations from 1,702 speakers. The first, ExVo-MultiTask,
requires participants to train a multi-task model to recognize expressed
emotions and demographic traits from vocal bursts. The second, ExVo-Generate,
requires participants to train a generative model that produces vocal bursts
conveying ten different emotions. The third, ExVo-FewShot, requires
participants to leverage few-shot learning incorporating speaker identity to
train a model for the recognition of 10 emotions conveyed by vocal bursts. This
paper describes the three tracks and provides performance measures for baseline
models using state-of-the-art machine learning strategies. The baseline for
each track is as follows, for ExVo-MultiTask, a combined score, computing the
harmonic mean of Concordance Correlation Coefficient (CCC), Unweighted Average
Recall (UAR), and inverted Mean Absolute Error (MAE) ($S_{MTL}$) is at best,
0.335 $S_{MTL}$; for ExVo-Generate, we report Fr\'echet inception distance
(FID) scores ranging from 4.81 to 8.27 (depending on the emotion) between the
training set and generated samples. We then combine the inverted FID with
perceptual ratings of the generated samples ($S_{Gen}$) and obtain 0.174
$S_{Gen}$; and for ExVo-FewShot, a mean CCC of 0.444 is obtained.

Audio and Speech Processing;Machine Learning;Sound