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This research article investigates speech emotion recognition using machine learning techniques, specifically comparing convolutional neural networks (conv1D) and random forest (RF) models. The study utilizes various audio features and a limited dataset, augmented with additional audio, to classify emotions such as calm, happy, sad, and angry, achieving a higher accuracy of 69% with RF. The findings highlight the challenges of accurately classifying emotions due to similarities in acoustic features among di

Author

amiinuddinghozali

Language

EN