Changing Speaker Identity

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dc.contributor.advisorOates, Tim
dc.contributor.authorBansal, Naveen
dc.contributor.departmentComputer Science and Electrical Engineering
dc.contributor.programComputer Science
dc.date.accessioned2021-01-29T18:12:31Z
dc.date.available2021-01-29T18:12:31Z
dc.date.issued2018-01-01
dc.description.abstractConvolutional Neural Networks (CNNs) have been highly successful in solving computer vision problems like object detection, object recognition, and texture syntheses, but little work has been done on changing speaker identity using CNNs. In this theses, two research areas, namely, neural style transfer and voice conversion, are explored to change speaker identity in audio. Two machine learning models based on convolutional neural networks are proposed and extensive experiments are carried out to evaluate their performance on speech datasets.
dc.formatapplication:pdf
dc.genretheses
dc.identifierdoi:10.13016/m233wq-rkp0
dc.identifier.other11862
dc.identifier.urihttp://hdl.handle.net/11603/20715
dc.languageen
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Theses and Dissertations Collection
dc.relation.ispartofUMBC Graduate School Collection
dc.relation.ispartofUMBC Student Collection
dc.sourceOriginal File Name: Bansal_umbc_0434M_11862.pdf
dc.subjectConvolutional Neural Network
dc.subjectNeural Style Transfer
dc.subjectVoice Conversion
dc.titleChanging Speaker Identity
dc.typeText
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