  This work is devoted to capturing Emirati-accented speech database (Arabic
United Arab Emirates database) in each of neutral and shouted talking
environments in order to study and enhance text-independent Emirati-accented
speaker identification performance in shouted environment based on each of
First-Order Circular Suprasegmental Hidden Markov Models (CSPHMM1s),
Second-Order Circular Suprasegmental Hidden Markov Models (CSPHMM2s), and
Third-Order Circular Suprasegmental Hidden Markov Models (CSPHMM3s) as
classifiers. In this research, our database was collected from fifty Emirati
native speakers (twenty five per gender) uttering eight common Emirati
sentences in each of neutral and shouted talking environments. The extracted
features of our collected database are called Mel-Frequency Cepstral
Coefficients (MFCCs). Our results show that average Emirati-accented speaker
identification performance in neutral environment is 94.0%, 95.2%, and 95.9%
based on CSPHMM1s, CSPHMM2s, and CSPHMM3s, respectively. On the other hand, the
average performance in shouted environment is 51.3%, 55.5%, and 59.3% based,
respectively, on CSPHMM1s, CSPHMM2s, and CSPHMM3s. The achieved average speaker
identification performance in shouted environment based on CSPHMM3s is very
similar to that obtained in subjective assessment by human listeners.
