Automatic Speech Emotion and Speaker Recognition Based on Hybrid GMM and FFBNN

Abstract

In this paper we present text dependent speaker recognition with an enhancement of detecting the emotion of the speaker prior using the hybrid FFBN and GMM methods. The emotional state of the speaker influences recognition system. Mel-frequency Cepstral Coefficient (MFCC) feature set is used for experimentation. To recognize the emotional state of a speaker Gaussian Mixture Model (GMM) is used in training phase and in testing phase Feed Forward Back Propagation Neural Network (FFBNN). Speech database consisting of 25 speakers recorded in five different emotional states: happy, angry, sad, surprise and neutral is used for experimentation. The results reveal that the emotional state of the speaker shows a significant impact on the accuracy of speaker recognition.