Fauzivy Reggiswarashari
Universitas Sebelas Maret

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Speech emotion recognition using 2D-convolutional neural network Fauzivy Reggiswarashari; Sari Widya Sihwi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6594-6601

Abstract

This research proposes a speech emotion recognition model to predict human emotions using the convolutional neural network (CNN) by learning segmented audio of specific emotions. Speech emotion recognition utilizes the extracted features of audio waves to learn speech emotion characteristics; one of them is mel frequency cepstral coefficient (MFCC). Dataset takes a vital role to obtain valuable results in model learning. Hence this research provides the leverage of dataset combination implementation. The model learns a combined dataset with audio segmentation and zero padding using 2D-CNN. Audio segmentation and zero padding equalize the extracted audio features to learn the characteristics. The model results in 83.69% accuracy to predict seven emotions: neutral, happy, sad, angry, fear, disgust, and surprise from the combined dataset with the segmentation of the audio files.