Abstract
The speech emotion recognition is a challenging and an exigent task in the field of data science. Existing studies have only focused on one-dimensional Convolutional Neural Network (CNN) architecture for speech emotion recognition. This one-dimensional architecture’s speech recognition accuracy is low when dealt with RAVDESS, TESS and URDU datasets using non-optimal parameters. To overcome this problem, this research work proposed an efficient two-dimensional CNN architecture with an optimized combination of parameters to achieve better accuracy. The proposed method is compared with Support Vector Machine (SVM) and one-dimensional CNN using RAVDESS, TESS and URDU datasets based on accuracy. Based on the conducted experiments, it can be seen that, the proposed method has outperformed with an accuracy of 76.08% and 99.68%, respectively.
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Acknowledgement
This research was supported by the Universiti Tun Hussein Onn Malaysia (UTHM) through the Multidisciplinary Research Grant (MDR) (Vote H494).
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Butt, S.A., Iqbal, U., Ghazali, R., Shoukat, I.A., Lasisi, A., Al-Saedi, A.K.Z. (2022). An Improved Convolutional Neural Network for Speech Emotion Recognition. In: Ghazali, R., Mohd Nawi, N., Deris, M.M., Abawajy, J.H., Arbaiy, N. (eds) Recent Advances in Soft Computing and Data Mining. SCDM 2022. Lecture Notes in Networks and Systems, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-00828-3_19
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