Emotion Recognition for Persian Speech Using Convolutional Neural Network and Support Vector Machine

Hashemi, Saeed; Ayat, Saeed

doi:10.30473/coam.2023.66718.1226

Document Type : Research Article

Authors

‎Department of Computer Engineering and Information Technology‎, ‎Payame Noor University (PNU)‎, ‎Tehran‎, ‎Iran

https://doi.org/10.30473/coam.2023.66718.1226

Abstract

The paper discusses the limitations of emotion recognition in Persian speech due to inefficient feature extraction and classification tools‎. ‎To address this‎, ‎we propose a new method for detecting hidden emotions in Persian speech with higher recognition accuracy‎. ‎The method involves four steps‎: ‎preprocessing‎, ‎feature description‎, ‎feature extraction‎, ‎and classification‎. ‎The input signal is normalized in the preprocessing step using single-channel vector conversion and signal resampling‎. ‎Feature descriptions are performed using Mel-Frequency Cepstral Coefficients and Spectro-Temporal Modulation techniques‎, ‎which produce separate feature matrices‎. ‎These matrices are then merged and used for feature extraction through a Convolutional Neural Network‎. ‎Finally‎, ‎a Support Vector Machine with a linear kernel function is used for emotion classification‎. ‎The proposed method is evaluated using the Sharif Emotional Speech dataset and achieves an average accuracy of 80.9% in classifying emotions in Persian speech‎.

Keywords

References

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Emotion Recognition for Persian Speech Using Convolutional Neural Network and Support Vector Machine

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Volume 8, Issue 2 Open Access PolicySummer-Autumn ‎2023December 2023Pages 85-105

Volume 8, Issue 2
Open Access Policy
Summer-Autumn ‎2023
December 2023
Pages 85-105