  This work focuses on Emirati speaker verification systems in neutral talking
environments based on each of First-Order Hidden Markov Models (HMM1s),
Second-Order Hidden Markov Models (HMM2s), and Third-Order Hidden Markov Models
(HMM3s) as classifiers. These systems have been evaluated on our collected
Emirati speech database which is comprised of 25 male and 25 female Emirati
speakers using Mel-Frequency Cepstral Coefficients (MFCCs) as extracted
features. Our results show that HMM3s outperform each of HMM1s and HMM2s for a
text-independent Emirati speaker verification. The obtained results based on
HMM3s are close to those achieved in subjective assessment by human listeners.
