  This work aims at investigating and analyzing speaker identification in each
unbiased and biased emotional talking environments based on a classifier called
Suprasegmental Hidden Markov Models (SPHMMs). The first talking environment is
unbiased towards any emotion, while the second talking environment is biased
towards different emotions. Each of these talking environments is made up of
six distinct emotions. These emotions are neutral, angry, sad, happy, disgust
and fear. The investigation and analysis of this work show that speaker
identification performance in the biased talking environment is superior to
that in the unbiased talking environment. The obtained results in this work are
close to those achieved in subjective assessment by human judges.
