  It is well known that speaker identification performs extremely well in the
neutral talking environments; however, the identification performance is
declined sharply in the shouted talking environments. This work aims at
proposing, implementing and testing a new approach to enhance the declined
performance in the shouted talking environments. The new proposed approach is
based on gender-dependent speaker identification using Suprasegmental Hidden
Markov Models (SPHMMs) as classifiers. This proposed approach has been tested
on two different and separate speech databases: our collected database and the
Speech Under Simulated and Actual Stress (SUSAS) database. The results of this
work show that gender-dependent speaker identification based on SPHMMs
outperforms gender-independent speaker identification based on the same models
and gender-dependent speaker identification based on Hidden Markov Models
(HMMs) by about 6% and 8%, respectively. The results obtained based on the
proposed approach are close to those obtained in subjective evaluation by human
judges.
