i-Vector Algorithm with Gaussian Mixture Model for Efficient Speech Emotion Recognition

Abstract

Emotions constitute an essential part of our existence as it exerts great influence on the physical as well as mental health of people. Emotions often play the role of a sensitive catalyst, which fosters lively interaction between human beings. Over the past few decades the focus of researchers on study of the emotional content of speech signals, has progressively increased. Many systems have been proposed to make the Speech Emotion Recognition (SER) process more correct and accurate. The objective of our research is to classify speech emotion implementing a comparatively new method-i-vector model. i-vector model has found much success in the areas of speaker identification, speech recognition and language identification. But it has not been much explored in recognition of emotion. This paper discusses the design of a speech emotion recognition system considering three important aspects. Firstly, i-vector model was implemented in processing extracted features for speech representation. Secondly, an appropriate classification scheme was designed using Gaussian Mixture Model (GMM), Maximum A Posteriori (MAP) adaptation and i-vector algorithm. Finally, the performance of this new system was evaluated using emotional speech database. Speech emotions were identified with this novel system and also with a conventional system and results were compared, which proved that our proposed system can identify speech emotions with less error and more accuracy.