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pro vyhledávání: '"Williams, Jennifer A."'
Privacy-preserving voice conversion aims to remove only the attributes of speech audio that convey identity information, keeping other speech characteristics intact. This paper presents a mechanism for privacy-preserving voice conversion that allows
Externí odkaz:
http://arxiv.org/abs/2409.14919
Autor:
Müller, Nicolas M., Kawa, Piotr, Hu, Shen, Neu, Matthias, Williams, Jennifer, Sperl, Philip, Böttinger, Konstantin
Voice faking, driven primarily by recent advances in text-to-speech (TTS) synthesis technology, poses significant societal challenges. Currently, the prevailing assumption is that unaltered human speech can be considered genuine, while fake speech co
Externí odkaz:
http://arxiv.org/abs/2402.06304
Most recent speech privacy efforts have focused on anonymizing acoustic speaker attributes but there has not been as much research into protecting information from speech content. We introduce a toy problem that explores an emerging type of privacy c
Externí odkaz:
http://arxiv.org/abs/2401.03936
Autor:
Müller, Nicolas M., Burgert, Maximilian, Debus, Pascal, Williams, Jennifer, Sperl, Philip, Böttinger, Konstantin
Machine-learning (ML) shortcuts or spurious correlations are artifacts in datasets that lead to very good training and test performance but severely limit the model's generalization capability. Such shortcuts are insidious because they go unnoticed d
Externí odkaz:
http://arxiv.org/abs/2310.19381
Privacy in speech and audio has many facets. A particularly under-developed area of privacy in this domain involves consideration for information related to content and context. Speech content can include words and their meaning or even stylistic mar
Externí odkaz:
http://arxiv.org/abs/2301.08925
Machine learning is a data-driven field, and the quality of the underlying datasets plays a crucial role in learning success. However, high performance on held-out test data does not necessarily indicate that a model generalizes or learns anything me
Externí odkaz:
http://arxiv.org/abs/2211.15510
This paper presents an analysis of speech synthesis quality achieved by simultaneously performing voice conversion and language code-switching using multilingual VQ-VAE speech synthesis in German, French, English and Italian. In this paper, we utiliz
Externí odkaz:
http://arxiv.org/abs/2203.14640
Deepfakes are synthetically generated media often devised with malicious intent. They have become increasingly more convincing with large training datasets advanced neural networks. These fakes are readily being misused for slander, misinformation an
Externí odkaz:
http://arxiv.org/abs/2203.15563
To study information processing in the brain, neuroscientists manipulate experimental stimuli while recording participant brain activity. They can then use encoding models to find out which brain "zone" (e.g. which region of interest, volume pixel or
Externí odkaz:
http://arxiv.org/abs/2202.10376
Autor:
Williams, Jennifer, Wehbe, Leila
Similar to how differences in the proficiency of the cardiovascular and musculoskeletal system predict an individual's athletic ability, differences in how the same brain region encodes information across individuals may explain their behavior. Howev
Externí odkaz:
http://arxiv.org/abs/2112.06048