Emotional Expression Detection in Spoken Language Employing Machine Learning Algorithms

• URL: http://arxiv.org/abs/2304.11040v1
• Date: Thu, 20 Apr 2023 17:57:08 GMT
• Title: Emotional Expression Detection in Spoken Language Employing Machine Learning Algorithms
• Authors: Mehrab Hosain, Most. Yeasmin Arafat, Gazi Zahirul Islam, Jia Uddin, Md. Mobarak Hossain, Fatema Alam
• Abstract summary: There are a variety of features of the human voice that can be classified as pitch, timbre, loudness, and vocal tone. It is observed in numerous incidents that human expresses their feelings using different vocal qualities when they are speaking. The primary objective of this research is to recognize different emotions of human beings by using several functions namely, spectral descriptors, periodicity, and harmonicity.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There are a variety of features of the human voice that can be classified as pitch, timbre, loudness, and vocal tone. It is observed in numerous incidents that human expresses their feelings using different vocal qualities when they are speaking. The primary objective of this research is to recognize different emotions of human beings such as anger, sadness, fear, neutrality, disgust, pleasant surprise, and happiness by using several MATLAB functions namely, spectral descriptors, periodicity, and harmonicity. To accomplish the work, we analyze the CREMA-D (Crowd-sourced Emotional Multimodal Actors Data) & TESS (Toronto Emotional Speech Set) datasets of human speech. The audio file contains data that have various characteristics (e.g., noisy, speedy, slow) thereby the efficiency of the ML (Machine Learning) models increases significantly. The EMD (Empirical Mode Decomposition) is utilized for the process of signal decomposition. Then, the features are extracted through the use of several techniques such as the MFCC, GTCC, spectral centroid, roll-off point, entropy, spread, flux, harmonic ratio, energy, skewness, flatness, and audio delta. The data is trained using some renowned ML models namely, Support Vector Machine, Neural Network, Ensemble, and KNN. The algorithms show an accuracy of 67.7%, 63.3%, 61.6%, and 59.0% respectively for the test data and 77.7%, 76.1%, 99.1%, and 61.2% for the training data. We have conducted experiments using Matlab and the result shows that our model is very prominent and flexible than existing similar works.

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