Speech Based Emotion Recognition Using Machine Learning

Abstract

Emotion is a natural feeling which is distinguished from reasoning or knowledge, it is a strong feeling derived from one’s circumstance or surroundings. With the increase in man to machine interaction, speech analysis has become an integral part in reducing the gap between physical and digital world. An important sub field within this domain is the recognition of emotion in speech signals, which was traditionally studied in linguistics and psychology. Speech emotion recognition is a field having diverse applications. When implemented the Speech Emotion Recognition (SER) will be able to understand different human emotion such as anger, fear, happiness, sadness etc. Speech is a medium of expression of one’s perspective or feelings to other. Emotion recognition from audio signal requires feature extraction and classifier training. The feature vector consists of elements of the audio signal which characterize speaker specific features such as tone, pitch, energy, which is crucial to train the classifier model to recognize a particular emotion accurately. Thus, with the help of SER we can make conversations between human and computer more realistic and natural. Automatic Speech Emotion Recognition (SER) is a current research topic in the field of Human Computer Interaction (HCI) with wide range of applications. The speech features such as, Mel Frequency cepstrum coefficients (MFCC) and Mel Energy Spectrum Dynamic Coefficients (MEDC) are extracted from speech utterance. The Support Vector Machine (SVM) is used as classifier to classify different emotional states such as anger, happiness, sadness, neutral, fear, from Berlin emotional database.