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This research is dedicated to improving text-independent Emirati-accentedspeaker identification performance in stressful talking conditions using threedistinct classifiers: First-Order Hidden Markov Models (HMM1s), Second-OrderHidden Markov Models (HMM2s), and Third-Order Hidden Markov Models (HMM3s). Thedatabase that has been used in this work was collected from 25 per genderEmirati native speakers uttering eight widespread Emirati sentences in each ofneutral, shouted, slow, loud, soft, and fast talking conditions. The extractedfeatures of the captured database are called Mel-Frequency CepstralCoefficients (MFCCs). Based on HMM1s, HMM2s, and HMM3s, averageEmirati-accented speaker identification accuracy in stressful conditions is58.6%, 61.1%, and 65.0%, respectively. The achieved average speakeridentification accuracy in stressful conditions based on HMM3s is so similar tothat attained in subjective assessment by human listeners.

Emirati-Accented Speaker Identification in Stressful Talking Conditions