International Conference on Computer and Knowledge Engineering

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Emotion Recognition In Persian Speech Using Deep Neural Networks

Authors :

Ali Yazdani¹ Hossein Simchi² Yasser Shekofteh³

1- Faculty of Computer Science and Engineering, Shahid Beheshti University 2- Faculty of Computer Science and Engineering, Shahid Beheshti University 3- Faculty of Computer Science and Engineering Shahid Beheshti University Tehran, Iran

Keywords :

Speech Emotion Recognition, Feature extraction, Deep Learning, Farsi Language, ShEMO dataset

Abstract :

Speech Emotion Recognition (SER) is of great importance in Human-Computer Interaction (HCI), as it provides a deeper understanding of the situation and results in better interaction. In recent years, various machine learning and Deep Learning (DL) algorithms have been developed to improve SER techniques. Recognition of the spoken emotions depends on the type of expression that varies between different languages. In this paper, to further study important factors in the Farsi language, we examine various DL techniques on a Farsi/Persian dataset, Sharif Emotional Speech Database (ShEMO), which was released in 2018. Using signal features in low- and high-level descriptions and different deep neural networks and machine learning techniques, Unweighted Accuracy (UA) of 65.20% and Weighted Accuracy (WA) of 78.29% is achieved.

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