  Speech Emotion Recognition (SER) is one of the essential perceptual methods
of humans in understanding the situation and how to interact with others,
therefore, in recent years, it has been tried to add the ability to recognize
emotions to human-machine communication systems. Since the SER process relies
on labeled data, databases are essential for it. Incomplete, low-quality or
defective data may lead to inaccurate predictions. In this paper, we fixed the
inconsistencies in Sharif Emotional Speech Database (ShEMO), as a Persian
database, by using an Automatic Speech Recognition (ASR) system and
investigating the effect of Farsi language models obtained from accessible
Persian text corpora. We also introduced a Persian/Farsi ASR-based SER system
that uses linguistic features of the ASR outputs and Deep Learning-based
models.
