  Affective speech analysis is an ongoing topic of research. A relatively new
problem in this field is the analysis of vocal bursts, which are nonverbal
vocalisations such as laughs or sighs. Current state-of-the-art approaches to
address affective vocal burst analysis are mostly based on wav2vec2 or HuBERT
features. In this paper, we investigate the use of the wav2vec successor
data2vec in combination with a multitask learning pipeline to tackle different
analysis problems at once. To assess the performance of our efficient multitask
learning architecture, we participate in the 2022 ACII Affective Vocal Burst
Challenge, showing that our approach substantially outperforms the baseline
established there in three different subtasks.
