SPEAKER IDENTIFICATION MODEL BASED ON DEEP NURAL NETWOKS

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dc.contributor.authorAhmed, S.R.
dc.contributor.authorAbbood, Z.A.
dc.contributor.authorFarhan, H.M.
dc.contributor.authorYasen, B.T.
dc.contributor.authorAhmed, M.R.
dc.contributor.authorDuru, A.D.
dc.date.accessioned2024-09-29T16:16:02Z
dc.date.available2024-09-29T16:16:02Z
dc.date.issued2022
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThis study aims is to establish a small system of text-independent recognition of speakers for a relatively small group of speakers at a sound stage. The fascinating justification for the International Space Station (ISS) to detect if the astronauts are speaking at a specific time has influenced the difficulty. In this work, we employed Machine Learning Applications. Accordingly, we used the Direct Deep Neural Network (DNN)-based approach, in which the posterior opportunities of the output layer are utilized to determine the speaker's presence. In line with the small footprint design objective, a simple DNN model with only sufficient hidden units or sufficient hidden units per layer was designed, thereby reducing the cost of parameters through intentional preparation to avoid the normal overfitting problem and optimize the algorithmic aspects, such as context-based training, activation functions, validation, and learning rate. Two commercially available databases, namely, TIMIT clean speech and HTIMIT multihandset communication database and TIMIT noise-added data framework, were tested for this reference model that we developed using four sound categories at three distinct signal-to-noise ratios. Briefly, we used a dynamic pruning method in which the conditions of all layers are simultaneously pruned, and the pruning mechanism is reassigned. The usefulness of this approach was evaluated on all the above contact databases. © 2022 Iraqi Journal for Computer Science and Mathematics. All rights reserved.en_US
dc.identifier.doi10.52866/ijcsm.2022.01.01.012
dc.identifier.endpage113en_US
dc.identifier.issn2788-7421
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85129168890en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage108en_US
dc.identifier.urihttps://doi.org/10.52866/ijcsm.2022.01.01.012
dc.identifier.urihttps://hdl.handle.net/20.500.14619/8814
dc.identifier.volume3en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherCollege of Education, Al-Iraqia Universityen_US
dc.relation.ispartofIraqi Journal for Computer Science and Mathematicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeep neural networken_US
dc.subjectDNNsen_US
dc.subjectMachine learningen_US
dc.subjectSpeaker identificationen_US
dc.titleSPEAKER IDENTIFICATION MODEL BASED ON DEEP NURAL NETWOKSen_US
dc.typeArticleen_US

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