  Recently, there have been tremendous research outcomes in the fields of
speech recognition and natural language processing. This is due to the
well-developed multi-layers deep learning paradigms such as wav2vec2.0,
Wav2vecU, WavBERT, and HuBERT that provide better representation learning and
high information capturing. Such paradigms run on hundreds of unlabeled data,
then fine-tuned on a small dataset for specific tasks. This paper introduces a
deep learning constructed emotional recognition model for Arabic speech
dialogues. The developed model employs the state of the art audio
representations include wav2vec2.0 and HuBERT. The experiment and performance
results of our model overcome the previous known outcomes.
