Emotion Recognition In Persian Speech Using Deep Neural Networks

TL;DR

This paper explores deep neural network techniques for recognizing emotions in Persian speech, achieving notable accuracy improvements on the ShEMO dataset, which enhances human-computer interaction in Farsi language contexts.

Contribution

It evaluates various deep learning methods on Persian speech emotion recognition, providing new insights into language-specific challenges and performance benchmarks.

Findings

Achieved 65.20% Unweighted Accuracy

Achieved 78.29% Weighted Accuracy

Demonstrated effectiveness of DL techniques on Persian speech

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% are achieved.