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pro vyhledávání: '"P, Böttinger"'
Publikováno v:
Interspeech 2024
Recent research has highlighted a key issue in speech deepfake detection: models trained on one set of deepfakes perform poorly on others. The question arises: is this due to the continuously improving quality of Text-to-Speech (TTS) models, i.e., ar
Externí odkaz:
http://arxiv.org/abs/2406.03512
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
Autor:
Müller, Nicolas M., Kawa, Piotr, Choong, Wei Herng, Casanova, Edresson, Gölge, Eren, Müller, Thorsten, Syga, Piotr, Sperl, Philip, Böttinger, Konstantin
Text-to-Speech (TTS) technology offers notable benefits, such as providing a voice for individuals with speech impairments, but it also facilitates the creation of audio deepfakes and spoofing attacks. AI-based detection methods can help mitigate the
Externí odkaz:
http://arxiv.org/abs/2401.09512
Akademický článek
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Neural networks build the foundation of several intelligent systems, which, however, are known to be easily fooled by adversarial examples. Recent advances made these attacks possible even in air-gapped scenarios, where the autonomous system observes
Externí odkaz:
http://arxiv.org/abs/2311.08539
Uncontrolled hypertension is a global problem that needs to be addressed. Despite the many mHealth solutions in the market, the nonadherence relative to intended use jeopardizes treatment success. Although investigating user experience is one of the
Externí odkaz:
http://arxiv.org/abs/2311.05434
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
Autor:
Alleva, Eugenia, Landi, Isotta, Shaw, Leslee J, Böttinger, Erwin, Fuchs, Thomas J, Ensari, Ipek
Clinical note classification is a common clinical NLP task. However, annotated data-sets are scarse. Prompt-based learning has recently emerged as an effective method to adapt pre-trained models for text classification using only few training example
Externí odkaz:
http://arxiv.org/abs/2310.20089
Current anti-spoofing and audio deepfake detection systems use either magnitude spectrogram-based features (such as CQT or Melspectrograms) or raw audio processed through convolution or sinc-layers. Both methods have drawbacks: magnitude spectrograms
Externí odkaz:
http://arxiv.org/abs/2308.11800
Critical points mark locations in the domain where the level-set topology of a scalar function undergoes fundamental changes and thus indicate potentially interesting features in the data. Established methods exist to locate and relate such points in
Externí odkaz:
http://arxiv.org/abs/2308.05710