Metaheuristic Algorithms for Enhancing Multicepstral Representation in Voice Spoofing Detection: An Experimental Approach

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.contributorWeierstrass Institute-
Autor(es): dc.contributorIslamic Azad University-
Autor(es): dc.creatorContreras, Rodrigo Colnago-
Autor(es): dc.creatorHeck, Gustavo Luiz-
Autor(es): dc.creatorViana, Monique Simplicio-
Autor(es): dc.creatordos Santos Bongarti, Marcelo Adriano-
Autor(es): dc.creatorZamani, Hoda-
Autor(es): dc.creatorGuido, Rodrigo Capobianco-
Data de aceite: dc.date.accessioned2025-08-21T18:02:55Z-
Data de disponibilização: dc.date.available2025-08-21T18:02:55Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/978-981-97-7181-3_20-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/304446-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/304446-
Descrição: dc.descriptionThe problem of voice spoofing detection is critical for identity authentication within biometric systems. Among the existing countermeasures, those based on soft computing have received attention from researchers in the last few years. However, it is known that spoofing representation is only effective when many features are used, which limits its applicability due to the curse of dimensionality. Accordingly, we focus on strategies to reduce the dimensionality of multicepstral features while maintaining reasonable accuracy in distinguishing between real and spoofed voices. Given the complexity of voice data, identifying and prioritizing the features with the highest information content is of utmost relevance. The study utilized four metaheuristic algorithms-GA, DA, PSO, and GWO for dimension reduction. The findings indicate that all algorithms, particularly GWO, exceed baseline performance levels. This demonstrates their efficacy in detecting voice spoofing. Moreover, it was found that certain combinations of cepstral coefficients when applied with principal component analysis projection, notably enhanced the model’s performance of voice spoofing detection.-
Descrição: dc.descriptionInstitute of Biosciences Letters and Exact Sciences São Paulo State University (UNESP), SP-
Descrição: dc.descriptionUniversity of São Paulo, SP-
Descrição: dc.descriptionFederal University of São Carlos, SP-
Descrição: dc.descriptionWeierstrass Institute-
Descrição: dc.descriptionFaculty of Computer Engineering Islamic Azad University-
Descrição: dc.descriptionBig Data Research Center Najafabad Branch Islamic Azad University-
Descrição: dc.descriptionInstitute of Biosciences Letters and Exact Sciences São Paulo State University (UNESP), SP-
Formato: dc.format247-262-
Idioma: dc.languageen-
Relação: dc.relationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectCepstral Features-
Palavras-chave: dc.subjectDimensionality Reduction-
Palavras-chave: dc.subjectMetaheuristic Algorithms-
Palavras-chave: dc.subjectSpoofing Detection-
Título: dc.titleMetaheuristic Algorithms for Enhancing Multicepstral Representation in Voice Spoofing Detection: An Experimental Approach-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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