  Speech signals are subjected to more acoustic interference and emotional
factors than other signals. Noisy emotion-riddled speech data is a challenge
for real-time speech processing applications. It is essential to find an
effective way to segregate the dominant signal from other external influences.
An ideal system should have the capacity to accurately recognize required
auditory events from a complex scene taken in an unfavorable situation. This
paper proposes a novel approach to speaker identification in unfavorable
conditions such as emotion and interference using a pre-trained Deep Neural
Network mask and speech VGG. The proposed model obtained superior performance
over the recent literature in English and Arabic emotional speech data and
reported an average speaker identification rate of 85.2\%, 87.0\%, and 86.6\%
using the Ryerson audio-visual dataset (RAVDESS), speech under simulated and
actual stress (SUSAS) dataset and Emirati-accented Speech dataset (ESD)
respectively.
