A Summary of Work on Emotional Voice Recognition with Machine Learning
Abstract
This chapter compares SER systems. This talk covers a theoretical definition, affective state categorization, and several techniques to express emotions. This inquiry requires an SER system with many classifiers and feature extraction methods. After extracting Mel-Frequency Cestrum Coefficients (MFCC) and Modulation Spectral (MS) properties from speech signals, they are utilized to train multiple classifiers to classify them. FS was used to find the most relevant feature subset. Emotion classification was solved using many machine learning models. The initial categorization of these seven sentiments uses an RNN classifier. After that, their results are compared to those from spoken audio signals emotion detection methods like Multivariate Linear Regression (MLR) and Support Vector Machine (SVM). Experimental data has been collected from Berlin and Spanish databases. Speaker Normalization (SN) and feature selection improve the accuracy of all Berlin database classifiers to 83%. In Spanish datasets, RNN classifiers without SN and with FS have the maximum accuracy (94%).
How to cite this article:
Kaur M. A Summary of Work on Emotional Voice Recognition with Machine Learning. J Adv Res Comp Graph Multim Tech. 2023; 5(1): 1-13.
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