In collaboration with Payame Noor University and the Iranian Society of Instrumentation and Control Engineers

Document Type : Research Article


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


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.


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