Context Independent Speaker Classification

Show simple item record Olshanetskyi, Borys 2020-02-25T15:33:50Z 2020-02-25T15:33:50Z 2020
dc.identifier.citation Olshanetskyi, Borys. Context Independent Speaker Classification : Master Thesis : manuscript rights / Borys Olshanetskyi ; Supervisor Oleksii Molchanovskyi ; Ukrainian Catholic University, Department of Computer Sciences. – Lviv : [s.n.], 2020. – 36 p. : ill. uk
dc.language.iso en uk
dc.subject Mel Spectrogram uk
dc.subject Convolutional Neural Networks uk
dc.subject Convolutional Kernels uk
dc.title Context Independent Speaker Classification uk
dc.type Preprint uk
dc.status Публікується вперше uk
dc.description.abstracten Speaker classification is an essential task in the machine learning domain, with many practical applications in identification and natural language processing. This work concentrates on speaker classification as a subtask of general speaker diarization for real-world conversation scenarios. We research the domain of modern speech processing and present the original speaker classification approach based on the recent developments in convolutional neural networks. Our method uses a spectrogram as input to the CNN classifier model, allowing it to capture spatial information about voice frequencies distribution. Presented results show beyond human ability performance and give strong prospects for future development. uk

Files in this item

This item appears in the following Collection(s)

Show simple item record



My Account