1 research outputs found
Emirati-Accented Speaker Identification in Stressful Talking Conditions
This research is dedicated to improving text-independent Emirati-accented
speaker identification performance in stressful talking conditions using three
distinct classifiers: First-Order Hidden Markov Models (HMM1s), Second-Order
Hidden Markov Models (HMM2s), and Third-Order Hidden Markov Models (HMM3s). The
database that has been used in this work was collected from 25 per gender
Emirati native speakers uttering eight widespread Emirati sentences in each of
neutral, shouted, slow, loud, soft, and fast talking conditions. The extracted
features of the captured database are called Mel-Frequency Cepstral
Coefficients (MFCCs). Based on HMM1s, HMM2s, and HMM3s, average
Emirati-accented speaker identification accuracy in stressful conditions is
58.6%, 61.1%, and 65.0%, respectively. The achieved average speaker
identification accuracy in stressful conditions based on HMM3s is so similar to
that attained in subjective assessment by human listeners.Comment: 6 pages, this work has been accepted in The International Conference
on Electrical and Computing Technologies and Applications, 2019 (ICECTA 2019