Performance of voice deepfake detection by humans and machines

The work will start with getting familiar with the basics of the problem of voice deep fake detection (DFD), terminology, available techniques, data and challenges (especially AVSpoof), with the history and state of the art techniques and tools for speaker recognition (wespeaker toolkit), with state of the art techniques and tools for personalized text to speech (pTTS) synthesis and voice conversion. The topic will then concentrate on the evaluation of human and machine performance in DFD, while concentrating on the motivation aspect of human subjects to simulate true attacks on people. The PhD will then progress in both (1) technical (advances in deepfake generation and detection technology) and (2) human-aspects. The work counts on collaboration with specialists from psychology and sociology.