Emotion Recognition of Stressed Speech using Teager Energy and Linear Prediction Features

In this paper, a Speech Emotion Recognition (SER) system is proposed using the feature combination of Teager Energy Operator (TEO) and Linear Prediction Coefficient (LPC) features as T-LPC feature extraction. The stressed speech signals which were not accurately recognized in the previous SER systems were recognized using the proposed methods. Gaussian Mixture Model (GMM) classifier is used to categorize the emotions of EMO-DB database in this analysis. The Stressed Speech Emotion Recognition (SSER) proposed using the T-LPC feature extraction technique acquired better performance compared to the existing Pitch, LPC, and LPC + Pitch feature based recognition systems. This proposed emotion recognition system can be used to motivate the students by finding their emotional state providing better accuracy compared to the existing ones.