EMARATI SPEAKER IDENTIFICATION

In this work we focus on Emarati speaker identification systems in neutral talking environments based on each of Vector Quantization (VQ), Gaussian Mixture Models (GMMs), and Hidden Markov Models (HMMs) as classifiers. These systems have been tested on our collected Emarati speech database which is composed of 25 male and 25 female Emarati speakers using MelFrequency Cepstral Coefficients (MFCCs). Our results yield an average text-dependent Emarati speaker identification performance of 100.00%, %99.81, and 99.69% based on VQ, GMMs, and HMMs, respectively. For text-independent systems, the average Emarati speaker identification performance based on VQ, GMMs, and HMMs is 94.48%, 86.55%, and 74.83%, respectively. The achieved results based on VQ are close to those obtained in subjective assessment by human listeners.