  The studies of predicting affective states from human voices have relied
heavily on speech. This study, indeed, explores the recognition of humans'
affective state from their vocal burst, a short non-verbal vocalization.
Borrowing the idea from the recent success of wav2vec 2.0, we evaluated
finetuned wav2vec 2.0 models from different datasets to predict the affective
state of the speaker from their vocal burst. The finetuned wav2vec 2.0 models
are then trained on the vocal burst data. The results show that the finetuned
wav2vec 2.0 models, particularly on an affective speech dataset, outperform the
baseline model, which is handcrafted acoustic features. However, there is no
large gap between the model finetuned on non-affective speech dataset and
affective speech dataset.
