Speech based Emotion Recognition using various Features and SVM Classifier

Abstract

In this paper methodology for human emotion recognizes by extracting the speech signal. This speaker-based emotion recognition system recognizes the four emotions namely happiness, sadness, fear and angry. Basically, aim of this system to recognize the emotions and estimate the various features namely formant frequency, energy, pitch and MFCC from speech signal. accuracy of emotion detection system using speech signal depends on types of feature used to extract unique characteristics in case of individual emotion recognition. emotions are showing effective changes in specific features and hence SVM (Support Vector Machine) classifier can show better performance. Individual emotion classification shows better performance in our work but in case of average it is considerably less i.e. 68%.

Feature Fusion for Emotion Recognition †

“…However, before selecting the model, it is important to keep a lookout for computational complexity and how the algorithm scales with different features, patterns, and categories. Common techniques for classification are SVM, KNN, CNN, and MLP [8][9][10][11][12][13][14].…”

“…Authors in reference [13] used two datasets: TESS and RAVDESS. The machinelearning technique used was DNN and the features extracted were MFCC.…”