DeepMind’s artificial intelligence talents have been working up capabilities for a consumer product. Sam Shead, Senior Technology Reporter for Business Insider UK, said Google applied software developed by DeepMind for use in its virtual assistant.
DeepMind, the AI company, has a version of a WaveNet system for American English and Japanese, according to a blog post published on Wednesday. They said, “we are proud to announce that an updated version of WaveNet is being used to generate the Google Assistant voices for US English and Japanese across all platforms.”
“Google has been slow to integrate DeepMind’s technology into its products, with just one data centre efficiency project announced so far, albeit on a global scale,” said Shead. “Now the company’s WaveNet neural network is being used to generate the Google Assistant voices for US English and Japanese.”
Google Assistant is a virtual personal assistant developed by Google.
Pocket-lint described Google Assistant as a voice-controlled smart assistant. “It’s considered an upgrade or an extension of Google Now – designed to be personal – while expanding on Google’s existing ‘OK Google’ voice controls.”
The DeepMind blog post was from Aäron van den Oord, research scientist, Tom Walters, research scientist, and Trevor Strohman, Google Speech software engineer.
The update they talk about is by the DeepMind WaveNet research and engineering teams, together with the Google Text-to-Speech team.
WaveNet has come a long way in a short time.
Just over a year ago, WaveNet was presented, a deep neural network generating raw audio waveforms and capable of producing speech.
How they built it: A convolutional neural network was trained on a large dataset of speech samples. The goal was more natural-sounding speech than in existing techniques. In their original paper, they said it “creates individual waveforms from scratch, one sample at a time, with 16,000 samples per second and seamless transitions between individual sounds.”
As the blog authors put it, “WaveNet showed promise but was not something we could deploy in the real world.” It was “too computationally intensive” for use in consumer products. The team got busy to improve the model. They said it now can run “at scale and is the first product to launch on Google’s latest TPU cloud infrastructure.”
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