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Re:Lyrebird – An API to copy the voice of anyone by ehsankia on Monday April 24, 2017 @03:51AM (#14182576)
Well, the question is, do they just need to throw more computational power / training at this algorithm or is that the peak of their implementation?

This is something Google has been working a lot on [1] and Baidu also recently posted about their results too [2]. We're definitely pretty close to passing the human detectable level.

[1] https://deepmind.com/blog/wavenet-generative-model-raw-audio...

[2] http://research.baidu.com/deep-voice-production-quality-text...

Re:Lyrebird – An API to copy the voice of anyone by backpropaganda on Monday April 24, 2017 @04:22AM (#14182696)
Google and Baidu have only demonstrated single speaker TTS. Lyrebird's the first to demonstrate being able to generate arbitrary voices. Since this came out of a research lab, I would guess that the quality would only improve if they are given more compute and data.
Re:Lyrebird – An API to copy the voice of anyone by jtbayly on Monday April 24, 2017 @07:00AM (#14183279)
The Google one is able to generate arbitrary voices based on the recordings used for training. So much so that they made it generate piano music.
Re:Lyrebird – An API to copy the voice of anyone by bambax on Monday April 24, 2017 @04:42AM (#14182773)
Maybe. However, most CGI (in big Hollywood productions with almost no budget limit) is still very detectable, so much so that the biggest productions try to do as much as possible with real props and real actors instead of CGI (cf. interviews about the last Fast and Furious movie).

The human mind seems to be better at this than most creators credit it for.

Re:Lyrebird – An API to copy the voice of anyone by truthexposer on Monday April 24, 2017 @03:59PM (#14187632)
I believe what makes the voices robotic is due to the little amount of audio they need to generate a "usuable" voice from the system.

Speech models usually use triphones, which turns out to be a huge amount of audio. This is particularly impressive because of how little data they need.

Google used their own datasets, which are most likely massive.