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The Efficacy of Deep Learning-Based Mixed Model for Speech Emotion Recognition

Mohammad Amaz Uddin1, Mohammad Salah Uddin Chowdury1, Mayeen Uddin Khandaker2,*, Nissren Tamam3, Abdelmoneim Sulieman4
1 Department of Computer Science and Engineering, BGC Trust University Bangladesh, Chittagong, 4381, Bangladesh
2 Centre for Applied Physics and Radiation Technologies, School of Engineering and Technology, Sunway University, Bandar Sunway, Selangor, 47500, Malaysia
3 Department of Physics, College of Sciences, Princess Nourah bint Abdulrahman University, P.O Box 84428, Riyadh, 11671, Saudi Arabia
4 Department of Radiology and Medical Imaging, Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia
* Corresponding Author: Mayeen Uddin Khandaker. Email:

Computers, Materials & Continua 2023, 74(1), 1709-1722. https://doi.org/10.32604/cmc.2023.031177

Received 12 April 2022; Accepted 23 May 2022; Issue published 22 September 2022

Abstract

Human speech indirectly represents the mental state or emotion of others. The use of Artificial Intelligence (AI)-based techniques may bring revolution in this modern era by recognizing emotion from speech. In this study, we introduced a robust method for emotion recognition from human speech using a well-performed preprocessing technique together with the deep learning-based mixed model consisting of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). About 2800 audio files were extracted from the Toronto emotional speech set (TESS) database for this study. A high pass and Savitzky Golay Filter have been used to obtain noise-free as well as smooth audio data. A total of seven types of emotions; Angry, Disgust, Fear, Happy, Neutral, Pleasant-surprise, and Sad were used in this study. Energy, Fundamental frequency, and Mel Frequency Cepstral Coefficient (MFCC) have been used to extract the emotion features, and these features resulted in 97.5% accuracy in the mixed LSTM + CNN model. This mixed model is found to be performed better than the usual state-of-the-art models in emotion recognition from speech. It also indicates that this mixed model could be effectively utilized in advanced research dealing with sound processing.

Keywords

Emotion recognition; Savitzky Golay; fundamental frequency; MFCC; neural networks

Cite This Article

M. A. Uddin, M. S. U. Chowdury, M. U. Khandaker, N. Tamam and A. Sulieman, "The efficacy of deep learning-based mixed model for speech emotion recognition," Computers, Materials & Continua, vol. 74, no.1, pp. 1709–1722, 2023.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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