An Improved MSER using Grid Search based PCA and Ensemble Voting Technique

Recognizing speech emotions is indeed a crucial aspect of human–computer interaction. However, developing a model that can accurately process multiple languages is one of the challenging tasks. The feature selection process plays a vital role in multilingual speech emotion recognition because it helps to reduce irrelevant features from each language, ultimately enhancing the performance of the model. This research aims to address this task in a more precise way. It achieves this by employing Grid Search based Principal Component Analysis and an ensemble voting classifier for multilingual speech emotion recognition. Here we mention three essential steps of recognizing emotion from a multilingual dataset. The first step involves feature extraction from speech signals, such as MFCC, root-mean-square, ZCR, flux, roll-off, Centroid, bandwidth, chroma, and fundamental frequency. The second step entails the selection of an essential feature subset by removing redundant and unnecessary features using Principal Component Analysis. We also utilize the Grid Search technique to determine the feature subset that would yield the highest accuracy. The third step encompasses SVM and Random Forest, that are widely recognized classifiers. Additionally, we propose an ensemble voting classifier. Our study compares the performance of these classifiers on three distinct corpora—RAVDESS, EMOVO, and SUBESCO with and without the feature selection strategy. The accuracy for RAVDESS EMOVO and SUBESCO dataset 74.30%, 79.66%, 87.64%, respectively. After comparing our proposed approach with other approaches mentioned in the literature survey, it became evident that our approach outperforms the rest.