Changing Speaker Identity
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Date
2018-01-01
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Department
Computer Science and Electrical Engineering
Program
Computer Science
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Distribution Rights granted to UMBC by the author.
Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan thorugh a local library, pending author/copyright holder's permission.
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan thorugh a local library, pending author/copyright holder's permission.
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
Abstract
Convolutional 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.