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September 28, 2025Deleted JournalOpen Access

Machine Learning Methods for Speech Emotion Recognition

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Machine learning models improve speech emotion recognition, highlighting the effectiveness of CNN and SVM classifiers.

Deep learning models, particularly CNN and CNN-LSTM hybrids, achieve significant recognition accuracy, with up to 86.7%.
Experimental results using the RAVDESS dataset validate the robustness of the proposed approach in classifying emotional cues.
Feature extraction utilizing spectral contrast and mel-frequency cepstral coefficients enhances classification performance.
Shallow classifiers like SVM and Random Forest show limitations when dealing with complex features, emphasizing deep learning's advantages.

Sucunuta et al. (2025) studied this question.

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