How to Create Fake-Voice Text-to-Speech With Deep Learning ft. Elon Musk, Trump, Obama, and Joe Rogan

TL;DR

Fake-voice text-to-speech can be created by training a neural network such as DC TTS on voice examples and then generating speech from text. The creator found DC TTS more effective than Tacotron and Tacotron 2 and highlights its deep convolutional architecture with guided attention. The technology can produce convincing impersonations, making online audio harder to trust; read on for the model choice, workflow, and risks.

Transcript

what's going on everybody in welcome to a video about neural networks that speak every voice in this video is generated from a neural network I know people will claim they could told me you I could hear the difference there's absolutely no way I would be fooled by this I have some of the best years not too big not too small just right give it long ... Read More

Key Insights

• 🤨 Neural networks have the potential to generate highly convincing fake audio and video content, raising concerns about the trustworthiness of information online.
• 🎮 The spread of fake audio and video can have significant implications, including the spread of misinformation, identity theft, and cyberattacks.
• 💁 Individuals must remain vigilant and critical in verifying the authenticity of the information they consume.
• 🧑‍🏭 Laws alone may not be sufficient to prevent the misuse of fake audio and video, as bad actors may not abide by legal regulations.

Questions & Answers

Q: How can you create fake-voice text-to-speech with deep learning?

The demonstrated approach uses voice examples to train a text-to-speech neural network and then generates speech from supplied text. The creator chose the DC TTS model and used an available code implementation based on a paper written in late 2017.

Q: Which text-to-speech model was used for the fake voices?

The creator used DC TTS after experimenting with multiple models. Tacotron and Tacotron 2 were also tested, but DC TTS produced the best results in this project.

Q: How does the DC TTS model generate speech?

DC TTS uses deep convolutional neural networks with guided attention. It maps one sequence, text, to another sequence, speech, without using a recurrent cell.

Q: Why use a convolutional neural network instead of a recurrent neural network?

The transcript says deep convolutional neural networks were outperforming recurrent neural networks on these sequence-to-sequence tasks. They were also training faster and producing better results, while recurrent networks required substantial data and trained slowly.

Q: How did traditional computer text-to-speech work?

Traditional systems commonly had someone record hundreds of hours of audio in a studio. Digital signal processing and hard-coded rules were then used to manage details such as pauses, sentences, and speech patterns.

Q: Can fake neural-network voices sound convincing?

The generated voice may sound convincing initially, although listeners might notice something wrong if they hear it long enough. The demonstration argues that this capability makes it harder to know whether online audio or video can be trusted.

Q: What risks come with fake audio and video?

The creator warns that fake audio and video could clog information channels and become part of cyberattacks. Because false information may spread before the truth emerges, some people may never encounter a later correction.

Q: Can laws prevent malicious voice impersonation?

The transcript says impersonating people for nefarious purposes is already illegal, so simply banning the technology is not necessarily the answer. Bad actors in other countries may ignore those laws, which is why citizens are urged to remain vigilant about information from online and social-media sources.

Summary & Key Takeaways

• Neural networks are capable of generating realistic voice and video content, leading to potential issues of trustworthiness in online information.

• The ability to create fake audio and video can be used for nefarious purposes, posing a threat to cybersecurity.

• It is crucial for individuals to be vigilant and critical of the information they consume online to protect themselves from the threat of fake audio and video content.