University of Bahrain
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A hybrid of Deep Neural Network and eXtreme Gradient Boosting for Automatic Speaker Identification

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dc.contributor.author Abdiche, Dehia
dc.contributor.author Harrar, Khaled
dc.date.accessioned 2022-03-09T07:51:07Z
dc.date.available 2022-03-09T07:51:07Z
dc.date.issued 2022-03-09
dc.identifier.uri https://journal.uob.edu.bh:443/handle/123456789/4595
dc.description.abstract This work consists of deploying a system for Speaker Identification (SI). SI is a system of recognition of the speaker’s speech signal. The most important thing in SI is to have a system that is able to extract and learn discriminative and relevant features for classification. Most research on SI has shown the effectiveness of Perceptual Linear Predictive (PLP) and Mel-Frequency Cepstral Coefficients (MFCC). Nevertheless, these extraction techniques exhibit identification errors when the speech signal is complex. To overcome this problem, this study proposes two features extraction techniques. The first technique uses Mel-Frequency Energy Coefficients (MFEC), the second technique is a hybrid approach combining MFEC and Convolutional Neural Network (CNN) used as features extractors. SI was performed using the features derived from the speech signals in the Voxforge database by both classifiers, namely CNNs and eXtreme Gradient Boosting (XGBoost). The proposed hybrid model using XGBoost-CNN achieved an accuracy of 99.45% demonstrating the effectiveness of this combination for SI. Moreover, a comparative study was carried out and revealed that the proposed model provided promising results and outperformed the existing methods in the literature using the Voxforge database. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.subject Speaker identification en_US
dc.subject MFEC en_US
dc.subject CNN en_US
dc.subject XGBoost en_US
dc.title A hybrid of Deep Neural Network and eXtreme Gradient Boosting for Automatic Speaker Identification en_US
dc.type Article en_US
dc.identifier.doi http://dx.doi.org/10.12785/ijcds/XXXXXX
dc.volume 11 en_US
dc.issue 1 en_US
dc.pagestart XXXX en_US
dc.pageend XXXX en_US
dc.contributor.authoraffiliation LIST laboratory, University M’Hamed Bougara of Boumerdes, Algeria en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US


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