Analysis of Emotion Recognition System through Speech Signal Using KNN & GMM Classifier

In machine interaction with human being is yet challenging task that machine should be able to identify and react to human non-verbal communication such as emotions which makes the human computer interaction become more natural. In present research area automatic emotion recognition using speech is an essential task which paid close attention. Speech signal is a rich source of information and it is an attractive and efficient medium due to its numerous features of expressing approach & extracting emotions through speech is possible. In this paper emotions is recognized through speech using spectral features such as Mel frequency cepstrum coefficient prosodic features like pitch , energy and were utilized & study is carried out using KNearest Neighbor classifiers and Gaussian mixture model classifier which is used for detection of six basic emotional states of speaker’s such as anger ,happiness , sadness , fear , disgust and neutral using Berlin emotional speech database.