  Given the significance of speech emotion recognition, numerous methods have
been developed in recent years to create effective and efficient systems in
this domain. One of these methods involves the use of pretrained transformers,
fine-tuned to address this specific problem, resulting in high accuracy.
Despite extensive discussions and global-scale efforts to enhance these
systems, the application of this innovative and effective approach has received
less attention in the context of Persian speech emotion recognition. In this
article, we review the field of speech emotion recognition and its background,
with an emphasis on the importance of employing transformers in this context.
We present two models, one based on spectrograms and the other on the audio
itself, fine-tuned using the shEMO dataset. These models significantly enhance
the accuracy of previous systems, increasing it from approximately 65% to 80%
on the mentioned dataset. Subsequently, to investigate the effect of
multilinguality on the fine-tuning process, these same models are fine-tuned
twice. First, they are fine-tuned using the English IEMOCAP dataset, and then
they are fine-tuned with the Persian shEMO dataset. This results in an improved
accuracy of 82% for the Persian emotion recognition system. Keywords: Persian
Speech Emotion Recognition, shEMO, Self-Supervised Learning
