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Hi HN, I'm one of the creators from Dessa of this project. If you haven't listened to it, we just released a longer clip of the RealTalk model[1]. In my opinio

by pippin 7y ago

Hi HN, I'm one of the creators from Dessa of this project. If you haven't listened to it, we just released a longer clip of the RealTalk model[1]. In my opinion it's even more compelling than these clips. One of fascinating parts of building this has been the questions we received while showing it to people. I'll note a few anecdotes specifically: "What is the difference between this and a real voice?" "Can I learn to discern fakes over time?" "Would we relate differently to a generative voice model posthumously, compared to current media forms like videos?" These aren't questions that we necessarily have answers to yet, but they're important discussions to have. [1] https://www.youtube.com/watch?v=DWK_iYBl8cA&feature=youtu.be https://www.youtube.com/watch?v=DWK_iYBl8cA&feature=youtu.be

failrate 7y ago
All of these generated works are very impressive. I think it is completely irresponsible to advance the state of the art in this field without simultaneously developing techniques to demonstrate that the generated work is artificial. Please develop validation tests while developing your generative techniques.
- gridlockd 7y ago
  Haven't read what they're doing, but chances are they are using an adversial neural network. The job of the adversarial network is to tell apart real from fake. The job of the neural network is to fool the adverserial network. Both are trained in tandem. One could imagine training another adverserial network that isn't used to train the network itself, and so will pick up on nuances that the original adversary doesn't pick up on. Anyone could do that, I don't think it's the author's responsibility. Somewhat related: https://keenlab.tencent.com/en/2019/03/29/Tencent-Keen-Security-Lab-Experimental-Security-Research-of-Tesla-Autopilot/ https://keenlab.tencent.com/en/2019/03/29/Tencent-Keen-Secur...
  - petrochukm 7y ago
    Doubt it. Generative-adversarial models have had a lot of success in image generation; however, the same cannot be said for speech synthesis. Unless they have figured out a new technique, they are probably using Tacotron 2 (https://ai.googleblog.com/2017/12/tacotron-2-generating-human-like-speech.html https://ai.googleblog.com/2017/12/tacotron-2-generating-huma...). Google's Tacotron 2 already achieved human-parity TTS without adversarial training as measured by MOS.
- hey-fuck-you 7y ago
  I think it is completely irresponsible to advance the state of the art in this field without simultaneously developing techniques to demonstrate that the generated work is artificial. Rot in hell, controlling scumbag. The problem of discerning and differentiating artificially generated content is your own. Content is content. Information is information. The fault of harm from information rests upon the harmed. Don't be harmed by bullshit, by dots, by scribbles, by marks hewn in stone. It is not the information that harms you. You harm yourself.
- deleted 7y ago
  [deleted]

by pippin 7y ago

failrate 7y ago

All of these generated works are very impressive. I think it is completely irresponsible to advance the state of the art in this field without simultaneously developing techniques to demonstrate that the generated work is artificial. Please develop validation tests while developing your generative techniques.

gridlockd 7y ago
Haven't read what they're doing, but chances are they are using an adversial neural network. The job of the adversarial network is to tell apart real from fake. The job of the neural network is to fool the adverserial network. Both are trained in tandem. One could imagine training another adverserial network that isn't used to train the network itself, and so will pick up on nuances that the original adversary doesn't pick up on. Anyone could do that, I don't think it's the author's responsibility. Somewhat related: https://keenlab.tencent.com/en/2019/03/29/Tencent-Keen-Security-Lab-Experimental-Security-Research-of-Tesla-Autopilot/ https://keenlab.tencent.com/en/2019/03/29/Tencent-Keen-Secur...
- petrochukm 7y ago
  Doubt it. Generative-adversarial models have had a lot of success in image generation; however, the same cannot be said for speech synthesis. Unless they have figured out a new technique, they are probably using Tacotron 2 (https://ai.googleblog.com/2017/12/tacotron-2-generating-human-like-speech.html https://ai.googleblog.com/2017/12/tacotron-2-generating-huma...). Google's Tacotron 2 already achieved human-parity TTS without adversarial training as measured by MOS.
hey-fuck-you 7y ago
I think it is completely irresponsible to advance the state of the art in this field without simultaneously developing techniques to demonstrate that the generated work is artificial. Rot in hell, controlling scumbag. The problem of discerning and differentiating artificially generated content is your own. Content is content. Information is information. The fault of harm from information rests upon the harmed. Don't be harmed by bullshit, by dots, by scribbles, by marks hewn in stone. It is not the information that harms you. You harm yourself.
deleted 7y ago
[deleted]