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Creating Novel Deepfake Speech Dataset
Sztolarik, Maroš ; Homoliak, Ivan (oponent) ; Firc, Anton (vedoucí práce)
In the recent years, deepfake technology has advanced to a point where it can convincingly mimic human speech, posing significant challenges in distinguishing between real and synthetic voices. In this thesis, we introduce a novel dataset comprising speech deepfakes generated using diffusion models. This dataset, created with two sophisticated text-to-speech tools, DiffSpeech and ProDiff, aims to provide insight into the threat that these new tools pose. Two more datasets are created with more mature tools, Glow-TTS and Tacotron2, to provide a point of comparison. Then all the generated samples are analyzed through two deepfake detectors in order to provide a direct comparison into how much of a threat each tool is to these detectors. The results show that even though the tools utilizing the diffusion models are threatening, the use of diffusion models did not provide these tools any meaningful advantage in evading the detection.
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