Speech Emotion Detection Through Live Calls

Abstract

Abstract: Speech emotion recognition is a popular study area right now, with the goal of enhancing human-machine connection. Most of the research being done in this field now classifies emotions into different groups by extracting discriminatory features. Most of the work done nowadays concerns verbal expressions used for lexical analysis and emotion recognition. In our project, emotions are categorized into the following categories: angry, calm, fearful, happy, and sad. Speech Emotion Recognition, often known as, SER, is a technology that takes advantage of the fact that tone and pitch in a speech frequently convey underlying emotions. The approach to assessing or anticipating a speaker's gender and emotions from their speech has been given in the proposed work. By graphing the waveform and spectrogram, convolutional neural networks are used to evaluate or predict gender and emotions. A CNN model is created using the input of 12162 samples to ordering to identify the emotions present in the speech. In our study, the suggested model's overall accuracy is calculated using only one feature, the MFCC from the speech, and the 4 datasets (RAVDESS, SAVEE, CREAMA-D, and TESS). The accuracy is first calculated for each emotion and gender before the overall accuracy is discovered.