  This work aims at intensifying text-independent speaker identification
performance in real application situations such as noisy and emotional talking
conditions. This is achieved by incorporating two different modules: a
Computational Auditory Scene Analysis CASA based pre-processing module for
noise reduction and cascaded Gaussian Mixture Model Convolutional Neural
Network GMM-CNN classifier for speaker identification followed by emotion
recognition. This research proposes and evaluates a novel algorithm to improve
the accuracy of speaker identification in emotional and highly-noise
susceptible conditions. Experiments demonstrate that the proposed model yields
promising results in comparison with other classifiers when Speech Under
Simulated and Actual Stress SUSAS database, Emirati Speech Database ESD, the
Ryerson Audio-Visual Database of Emotional Speech and Song RAVDESS database and
the Fluent Speech Commands database are used in a noisy environment.
