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

Novel Cascaded Gaussian Mixture Model-Deep Neural Network Classifier for Speaker Identification in Emotional Talking Environments