Infant Mood Prediction and Emotion Classification with Different Intelligent Models

Abstract

In this paper, we have analysed the cries of infants aged 0 to 6 months and have tried predicting emotions which might be a tool of communication. The present work carried out is mainly for analysing infant cries to predict emotions of hunger, discomfort, and belly pain. The system described here involves the Mel Frequency Cepstral Coefficients (MFCC) feature extraction technique and consecutive processing of various classification models such as Decision Tree, Random Forest, Support Vector Machine (SVM), and Logistic Regression. After comparing the results from all the mentioned classifiers, we have concluded that for an infant cry analysis, SVM and Random Forest Classification gives the most accurate output of 91%.