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

Uddin, Mohammad Amaz and Chowdury, Mohammad Salah Uddin and Khandaker, Mayeen Uddin * and Tamam, Nissren and Sulieman, Abdelmoneim (2022) The Efficacy of Deep Learning-Based Mixed Model for Speech Emotion Recognition. Computers, Materials & Continua, 74 (1). pp. 1709-1722. ISSN 1546-2226

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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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