Speech Emotion Classification using Machine Learning

In recent years, the interaction between humans and machines has become an issue of concern. This paper results from study of various researches related to the investigation of the six basic human emotions which include anger, dislike, fear, happiness, sadness and surprise. [1, 3] Feature extraction is done from various voice utterances recorded from different persons. The various features like pitch, energy, fundamental frequency are extracted from the utterances using respective feature extraction algorithms. After feature extraction procedure, the extracted features are classified under the basic six emotions using various machine learning algorithms. [1, 3, 4]And using the different algorithms the classification accuracy is measured for each algorithm respectively. Various acoustic and prosodic features are extracted from the speech recorded and then classified under different emotional category using machine learning tools. [7] This paper discusses how feature extraction through speech samples and then classification of the extracted features under different emotions is performed.