Voice Anonymization
Protecting Identity Through Voice Transformation [Archived]
Research
In this project, we develop a system for voice anonymization using a voice conversion (VC) approach, in which we convert the vocal identity of an utterance to sound like another person without changing the linguistic or prosodic content. Using a state-of-the-art deep neural network VC model, we are able to transform any speech utterance to sound like any target speaker given a sample of the target speaker’s speech. We further explore how listening to speech anonymized in this way affects peoples’ perception of the content that is conveyed, both from the point of view of the listener and the original speaker.
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