Mohammed Nasser Ba-Hutair

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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(More)
This work is aimed at exploiting Second-Order Circular Suprasegmental Hidden Markov Models (CSPHMM2s) as classifiers to enhance talking condition recognition in stressful and emotional talking environments (completely two separate environments). The stressful talking environment that has been used in this work uses Speech Under Simulated and Actual Stress(More)
Finding the optimal path in multi-hop wireless networks has gained considerable interest in the recent literature. Standard routing methods such as Ad-hoc On-Demand Distance Vector Routing (AODV) and Dynamic Source Routing (DSR) are based on link-level abstraction of the network without fully considering the impact of the physical layer. Recent studies have(More)
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