![]() ![]() This is based on a generic voice for which a lot of voice material, at least 24 hours of audio, has to be available. In a first step, the model must be taught to read in a specified language and to be able to reproduce what has been read. ![]() The approach of current tools is to read out text in the voice of a selected person. And the more such material is available, the better the audio deepfake will be. To create an audio deepfake, clear recordings of a speaker, preferably without interruptions, ambient or background noise, are needed. However, the approach to processing voice material is different because of the starting point. What is similar is that audio deepfakes are based on the same principles of computation with neural networks. The procedure for creating audio deepfakes is similar to visual deepfakes but still different. This article gives an overview of deepfakes for voice recordings. However, deepfakes can be created not only for videos or images, but this possibility also exists for audio recordings. At that time, only video or image deepfakes were considered. In 20, in a series of articles, deepfakes were analysed as well as own deepfakes were created. ![]() Nevertheless, companies should think about how they will deal with for example fake calls in the future.With currently publicly available tools, a solid technical understanding is required to create audio deepfakes, the general public cannot currently easily create an audio deepfake.This uses the so-called text-to-speech method.In addition to visual deepfakes, there is also the possibility of creating deepfakes from audio. ![]()
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