Investigating fuzzy methods for multilingual speaker identification

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dc.contributor.advisorAbreu, Marjory Cristiany da Costa
dc.contributor.advisorIDpt_BR
dc.contributor.authorLima, Thales Aguiar de
dc.contributor.authorIDpt_BR
dc.contributor.referees1Santin, Altair Olivo
dc.contributor.referees1IDpt_BR
dc.contributor.referees2Pereira, Mônica Magalhães
dc.contributor.referees2IDpt_BR
dc.date.accessioned2020-10-05T17:14:37Z
dc.date.available2020-10-05T17:14:37Z
dc.date.issued2020-08-27
dc.description.resumoSpeech is a crucial ability for humans to interact and communicate. Speech-based technologies are becoming more popular with speech interfaces, real-time translation, and budget healthcare diagnosis. Besides, the use of voice for system identification is an important and relevant topic. There are several ways of doing it, but most are dependent on the language the user speaks. However, if the idea is to create an all inclusive and reliable system that uses speech as its input, we must take into account that people can and will speak different languages and accents. This research evaluates closed-set text-independent speaker identification systems on a multilingual setup, including both fuzzy and crisp models. Our experiments are performed using three widely spoken languages which are Portuguese, English, and Chinese. Then, we extracted 13-MFCCs, along with log-Energy and its respective delta and delta-delta from signals to use as our feature vector. We adopted four classifiers: Fuzzy C-Means, Fuzzy k-Nearest Neighbours, k-Nearest Neighbours, and Support Vector Machines. Initial tests indicated the systems have certain robustness on multiple languages. Where results with more languages decreases our accuracy; however our investigation suggests these impacts are from number of classes.pt_BR
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESpt_BR
dc.identifier.citationLIMA, Thales Aguiar de. Investigating fuzzy methods for multilingual speaker identification. 2020. 66f. Dissertação (Mestrado em Sistemas e Computação) - Centro de Ciências Exatas e da Terra, Universidade Federal do Rio Grande do Norte, Natal, 2020.pt_BR
dc.identifier.urihttps://repositorio.ufrn.br/handle/123456789/30245
dc.languagept_BRpt_BR
dc.publisherUniversidade Federal do Rio Grande do Nortept_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.initialsUFRNpt_BR
dc.publisher.programPROGRAMA DE PÓS-GRADUAÇÃO EM SISTEMAS E COMPUTAÇÃOpt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectSpeaker identificationpt_BR
dc.subjectSpeaker recognitionpt_BR
dc.subjectFuzzypt_BR
dc.subjectSignal processingpt_BR
dc.subjectMultilingual speech systemspt_BR
dc.subjectPortuguesept_BR
dc.subjectEnglishpt_BR
dc.subjectMandarinpt_BR
dc.titleInvestigating fuzzy methods for multilingual speaker identificationpt_BR
dc.typemasterThesispt_BR

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