A Deep Learning Approach based on Ensemble Classification Pipeline and Interpretable Logical Rules for Bilingual Fake Speech Recognition

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dc.authoridKarasulu, Bahadır / 0000-0001-8524-874X
dc.contributor.authorBoztepe, Emre Beray
dc.contributor.authorKarasulu, Bahadır
dc.date.accessioned2025-05-29T02:57:21Z
dc.date.available2025-05-29T02:57:21Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractThe essential steps of our study are to quantify and classify the differences between real and fake speech signals. In this scope, the main aim is to use the salient feature learning ability of deep learning in our study. With the use of ensemble classification pipeline, the interpretable logical rules were used for generalized reasoning with the class activation maps to discriminate the different speech classes as correctly. Fake audio samples were generated by using Deep Convolutional Generative Adversarial Neural Network. Our experiments were conducted on three different language dataset such as Turkish, English languages and Bilingual. As a result of higher classification and recognition accuracy with the use of classification pipeline as compiled into a majority voting-based ensemble classifier, the experimental results were obtained for each individual language performance approximately as 90% for training and as 80.33% for testing stages for pipeline, and it reached as 73% for majority voting results considered together with the appropriate test cases as well. To extract semantically rich rules, an interpretable logical rules infrastructure was used to infer the correct fake speech from class activations of deep learning's generative model. Discussion and conclusion based on scientific findings are included in our study.
dc.identifier.doi10.35378/gujs.1357317
dc.identifier.endpage97
dc.identifier.issn2147-1762
dc.identifier.issue1
dc.identifier.scopus2-s2.0-86000744534
dc.identifier.scopusqualityQ2
dc.identifier.startpage75
dc.identifier.urihttps://doi.org/10.35378/gujs.1357317
dc.identifier.urihttps://hdl.handle.net/20.500.12428/30024
dc.identifier.volume38
dc.identifier.wosWOS:001441442000004
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherGazi University
dc.relation.ispartofGazi University Journal of Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250529
dc.subjectEnsemble classifier
dc.subjectMachine learning
dc.subjectDeep learning
dc.subjectSpeech recognition
dc.subjectSpeech analysis
dc.titleA Deep Learning Approach based on Ensemble Classification Pipeline and Interpretable Logical Rules for Bilingual Fake Speech Recognition
dc.typeArticle

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