Open Access

ARTICLE

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

Mohammad Amaz Uddin¹, Mohammad Salah Uddin Chowdury¹, Mayeen Uddin Khandaker^2,*, 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.

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Keywords

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