Fake speech: Generating and detecting convincing voice impersonations by humans and machines

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This doctoral project aims to target the criminal use of deepfake technology in the audio domain, namely fake speech(i.e. impersonated speech). While deepfake applications are becoming more well-known to the public in recent years, thegeneral perception of deepfake still relates to visual impersonation, i.e. digitally transplanting an individual's (typically afamous person) face on top of another person's face, and often with considerable success as deepfaked images andvideos are becoming more convincing. However, although the domain of fake speech is less well-known to the generalpublic, it nevertheless presents real problems and consequences to society. Hence, this project will tackle the problem offake speech through a four-way permutation: i) Humans detecting fake speech generated by humans ii) Humansdetecting fake speech generated by machines iii) Machines detecting fake speech generated by humans iv) Machinesdetecting fake speech generated be machines By improving the rate of fake speech detection in each of the fourscenarios, it is anticipated that the results arising from this project can form a part of an overall eclectic effort to combatfake speech.