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This tutorial combines the theory and practical application of Deep Neural Networks (DNNs) for Text-to-Speech (TTS). It illustrates how DNNs are rapidly advancing the performance of all areas of TTS, including waveform generation and text processing, using a variety of model architectures. We link the theory to implementation with the Open Source Merlin toolkit.
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Deep learning has been successfully applied to solve various complex problems. However, deep learning algorithms have also been employed to create deepfakes (e.g. fake audios, images and videos) for misleading information. Deepfakes are increasingly detrimental to privacy, society security and even democracy. This talk first introduces the state-of-the-art voice generation algorithms which can be leveraged to manipulate or create audio audio content. Following by an overview of the audio deepfake detection efforts done by the research community through the ASVspoof challenges, the talk concludes with a discussion of the challenges in audio deepfake detection.
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This is a panel discussion on Dis/Misinformation Analysis and Countermeasures with Prof. Mauro Barni, Prof. Kazutoshi Sasahara and Prof. Yuhong Liu. This is part of the special issue.
Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.