Saeed Hashemi; Saeed Ayat
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 ...
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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.
