  The technical report presents our emotion recognition pipeline for
high-dimensional emotion task (A-VB High) in The ACII Affective Vocal Bursts
(A-VB) 2022 Workshop \& Competition. Our proposed method contains three stages.
Firstly, we extract the latent features from the raw audio signal and its
Mel-spectrogram by self-supervised learning methods. Then, the features from
the raw signal are fed to the self-relation attention and temporal awareness
(SA-TA) module for learning the valuable information between these latent
features. Finally, we concatenate all the features and utilize a
fully-connected layer to predict each emotion's score. By empirical
experiments, our proposed method achieves a mean concordance correlation
coefficient (CCC) of 0.7295 on the test set, compared to 0.5686 on the baseline
model. The code of our method is available at
https://github.com/linhtd812/A-VB2022.
