  This research is an effort to present an effective approach to enhance
text-independent speaker identification performance in emotional talking
environments based on novel classifier called cascaded Gaussian Mixture
Model-Deep Neural Network (GMM-DNN). Our current work focuses on proposing,
implementing and evaluating a new approach for speaker identification in
emotional talking environments based on cascaded Gaussian Mixture Model-Deep
Neural Network as a classifier. The results point out that the cascaded GMM-DNN
classifier improves speaker identification performance at various emotions
using two distinct speech databases: Emirati speech database (Arabic United
Arab Emirates dataset) and Speech Under Simulated and Actual Stress (SUSAS)
English dataset. The proposed classifier outperforms classical classifiers such
as Multilayer Perceptron (MLP) and Support Vector Machine (SVM) in each
dataset. Speaker identification performance that has been attained based on the
cascaded GMM-DNN is similar to that acquired from subjective assessment by
human listeners.
