  As a common way of emotion signaling via non-linguistic vocalizations, vocal
burst (VB) plays an important role in daily social interaction. Understanding
and modeling human vocal bursts are indispensable for developing robust and
general artificial intelligence. Exploring computational approaches for
understanding vocal bursts is attracting increasing research attention. In this
work, we propose a hierarchical framework, based on chain regression models,
for affective recognition from VBs, that explicitly considers multiple
relationships: (i) between emotional states and diverse cultures; (ii) between
low-dimensional (arousal & valence) and high-dimensional (10 emotion classes)
emotion spaces; and (iii) between various emotion classes within the
high-dimensional space. To address the challenge of data sparsity, we also use
self-supervised learning (SSL) representations with layer-wise and temporal
aggregation modules. The proposed systems participated in the ACII Affective
Vocal Burst (A-VB) Challenge 2022 and ranked first in the "TWO'' and "CULTURE''
tasks. Experimental results based on the ACII Challenge 2022 dataset
demonstrate the superior performance of the proposed system and the
effectiveness of considering multiple relationships using hierarchical
regression chain models.
