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The Technical Side of Deep Fakes

Code[ish]

Release Date: 12/01/2020

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Julián Duque is a Lead Developer Advocate at Salesforce and Heroku, and he's continuing a previous discussion with some members of Respeecher. Respeecher has created AI software which works within the speech-to-speech domain: it takes one voice and makes it sound exactly like another. Dmytro Bielievtsov, its CTO and co-founder, explains the practical uses of the software, such as re=recording the lines of an actor who is unavailable, or bringing historical figures to life in a museum.

In terms of sophistication, there are quite a few speech ML models already available on the Internet. The best source of audio to duplicate the speech patterns of a famous person is to grab an audiobook and pass it through one of these pre-existing models. But these models produce outputs which are poor in quality. That's one reason that speech-to-speech is hard to fake. The variation in our mouths and speech patterns, not to mention the emotive qualities, make the process of creating duplicate voices extremely difficult to pull off. One way Respeecher data can't be faked is by the fact that they position themselves as a B2B business, dealing with studios and other large estates which have access to immense amounts of hard-to-acquire sound data. The likelihood of another entity abusing a well-known voice is close to none. Another feature is that the audio is watermarked. Certain "artifacts" are embedded into the audio, which are imperceptible to humans, but easily identifiable by a computer program.

There's a consortium of several companies working on synthesized media who strategize in Slack on various ways to keep the tech from being misused. As well, Dmytro believes that there needs to more investment in education, to let people know that such technology exists, and to therefore be a bit suspicious with media they encounter online.