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

A Persian ASR-based SER: Modification of Sharif Emotional Speech Database and Investigation of Persian Text Corpora