Novel hybrid DNN approaches for speaker verification in emotional and stressful talking environments

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dc.authorid0000-0003-1840-9958en_US
dc.authorid0000-0002-7201-6963en_US
dc.authorid0000-0001-7856-9342en_US
dc.authorid0000-0003-1570-0897en_US
dc.contributor.authorShahin, Ismail
dc.contributor.authorNassif, Ali Bou
dc.contributor.authorNemmour, Nawel
dc.contributor.authorElnagar, Ashraf
dc.contributor.authorAlhudhaif, Adi
dc.contributor.authorPolat, Kemal
dc.date.accessioned2023-05-29T08:01:26Z
dc.date.available2023-05-29T08:01:26Z
dc.date.issued2021en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractIn this work, we conducted an empirical comparative study of the performance of text-independent speaker verification in emotional and stressful environments. This work combined deep models with shallow architecture, which resulted in novel hybrid classifiers. Four distinct hybrid models were utilized: deep neural network-hidden Markov model (DNN-HMM), deep neural network-Gaussian mixture model (DNN-GMM), Gaussian mixture model-deep neural network (GMM-DNN), and hidden Markov model-deep neural network (HMM-DNN). All models were based on novel implemented architecture. The comparative study used three distinct speech datasets: a private Arabic dataset and two public English databases, namely Speech Under Simulated and Actual Stress (SUSAS) and Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). The test results of the aforementioned hybrid models demonstrated that the proposed HMM-DNN leveraged the verification performance in emotional and stressful environments. Results also showed that HMM-DNN outperformed all other hybrid models in terms of equal error rate (EER) and area under the curve (AUC) evaluation metrics. The average resulting verification system based on the three datasets yielded EERs of 7.19, 16.85, 11.51, and 11.90% based on HMM-DNN, DNN-HMM, DNN-GMM, and GMM-DNN, respectively. Furthermore, we found that the DNN-GMM model demonstrated the least computational complexity compared to all other hybrid models in both talking environments. Conversely, the HMM-DNN model required the greatest amount of training time. Findings also demonstrated that EER and AUC values depended on the database when comparing average emotional and stressful performances.en_US
dc.description.sponsorshipThe authors would like to convey their thanks and appreciation to the ''University of Sharjah'' for supporting the work through the research group-Machine Learning and Arabic Language Processing.en_US
dc.identifier.citationShahin, I., Nassif, A. B., Nemmour, N., Elnagar, A., Alhudhaif, A., & Polat, K. (2021). Novel hybrid DNN approaches for speaker verification in emotional and stressful talking environments. Neural Computing and Applications, 33(23), 16033-16055.en_US
dc.identifier.doi10.1007/s00521-021-06226-w
dc.identifier.endpage16055en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue23en_US
dc.identifier.scopus2-s2.0-85108600276en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage16033en_US
dc.identifier.urihttp://dx.doi.org/10.1007/s00521-021-06226-w
dc.identifier.urihttps://hdl.handle.net/20.500.12491/10984
dc.identifier.volume33en_US
dc.identifier.wosWOS:000664435200004en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherSPRINGER LONDON LTDen_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeep Neural Networken_US
dc.subjectEmotional Talking Environmenten_US
dc.subjectHybrid Modelsen_US
dc.subjectIdentificationen_US
dc.subjectGenderen_US
dc.subjectCuesen_US
dc.titleNovel hybrid DNN approaches for speaker verification in emotional and stressful talking environmentsen_US
dc.typeArticleen_US

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