Enhancing the accuracy in classifying human emotion via speech recognition using novel support vector machine compared with multi-layer perceptron classifier.

Support Vector Machine and Multi-Layer Perceptron Classifier were used to improve the accuracy of identifying human speech based on emotions. The goal of the paper is to improve the accuracy of speech emotion classification. Support Vector Machine (SVM) and Multi-Layer Perceptron Classifier are the two groups in this study (MLPC). Each group has a sample size of ten people, and the study parameters are alpha = 0.05, beta = 0.2, and G power = 0.8. Their accuracies are also compared to one another using different sample sizes. With p = 0.202, the Support Vector Machine is 81.8 percent more accurate than the Multi-Layer Perceptron Classifier in classifying human voice emotion. The SVM model outperformsthe MLPC when it comes to detecting human emotion through speech. It can also be thought of as a superior choice for categorizing speech emotion. [ABSTRACT FROM AUTHOR]