The ACII Affective Vocal Bursts Workshop & Competition is focused on
understanding multiple affective dimensions of vocal bursts: laughs, gasps,
cries, screams, and many other non-linguistic vocalizations central to the
expression of emotion and to human communication more generally. This year's
competition comprises four tracks using a large-scale and in-the-wild dataset
of 59,299 vocalizations from 1,702 speakers. The first, the A-VB-High task,
requires competition participants to perform a multi-label regression on a
novel model for emotion, utilizing ten classes of richly annotated emotional
expression intensities, including; Awe, Fear, and Surprise. The second, the
A-VB-Two task, utilizes the more conventional 2-dimensional model for emotion,
arousal, and valence. The third, the A-VB-Culture task, requires participants
to explore the cultural aspects of the dataset, training native-country
dependent models. Finally, for the fourth task, A-VB-Type, participants should
recognize the type of vocal burst (e.g., laughter, cry, grunt) as an 8-class
classification. This paper describes the four tracks and baseline systems,
which use state-of-the-art machine learning methods. The baseline performance
for each track is obtained by utilizing an end-to-end deep learning model and
is as follows: for A-VB-High, a mean (over the 10-dimensions) Concordance
Correlation Coefficient (CCC) of 0.5687 CCC is obtained; for A-VB-Two, a mean
(over the 2-dimensions) CCC of 0.5084 is obtained; for A-VB-Culture, a mean CCC
from the four cultures of 0.4401 is obtained; and for A-VB-Type, the baseline
Unweighted Average Recall (UAR) from the 8-classes is 0.4172 UAR.

本文介绍 ACII 情感语音爆发赛事和四个任务，分别为 A-VB-High, A-VB-Two, A-VB-Culture, A-VB-Type，使用机器学习方法对情感识别进行建模与训练，并在大规模数据集上进行了基准测试。