Verifying speakers in emotional environments

This work is devoted to proposing, implementing and evaluating a two-stage approach to verify speakers in emotional environments using their emotions (emotion-dependent speaker verification problem) based on Hidden Markov Models (HMMs). The results of this work show that verifying speakers from their emotions gives promising results with a significant improvement over emotion-independent speaker verification. The emotional environments in this work are composed of six basic emotions. These emotions are: neutral, angry, sad, happy, disgust and fear. The results obtained based on the proposed approach are close to those obtained in subjective assessment by human judges.