Google Says Voice Search Faster, More Accurate

If you use spoken commands to initiate Google searches on your iPhone or Android device, you might notice that the results you're getting are not only more accurate but coming in faster than ever. That's because Google researchers have developed a new way to combine acoustic models with machine learning for improved handling of voice queries, even when there's background noise.

The improved capabilities are the latest of several updates Google has made to its voice recognition and transcription tools in recent months. Earlier this month, Google rolled out a new transcription tool for Docs in Google Chrome with support for more than 40 languages. And in July, the company's software engineers announced that better neural network modeling had reduced transcription errors by 49 percent in Google Voice and the company's Project Fi phone service.

In a blog post yesterday, members of the Google Speech Team said that they'd achieved further improvements in how machines can "understand" spoken language through new refinements to recurrent neural network modeling. Recurrent neural networks (RNNs) describe machine intelligence that enables dynamic behavior . . . in this case, accurately "hearing" spoken words on the fly in real time.

New Models Are 'Blazingly Fast'

The improved RNNs are "more accurate, especially in noisy environments, and they are blazingly fast," according to members of the team. They also require "much lower computational resources," they noted.

Ha┼čim Sak, one of the team members, posted a video on YouTube showing how Google's improved model helps a computer recognize a simple sentence like, "How cold is it outside?"

Most humans processing those words wouldn't think twice about how their brains put together each individual sound, or phoneme, in the sentence. But it takes advanced neural networks' acoustic models using even more advanced concepts like "connectionist temporal classification" and "sequence discriminative...

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