Microsoft claims breakthrough in real-time translation

May 28, 2014
On Tuesday at the inaugural Code Conference in Rancho Palos Verdes, Calif., Microsoft CEO Satya Nadella sat down to talk with Re/code's Kara Swisher and Walt Mossberg. Credit: Asa Mathat – Re/code.

Microsoft has announced what it called a breakthrough in real-time voice translation and said it would offer a test version through its Skype messaging unit before the end of 2014.

The US tech giant demonstrated the new Skype Translator at the Code Conference, saying it fulfills a vision of the "universal translator" in the Star Trek science fiction series.

"The Star Trek vision for a Universal Translator isn't a galaxy away, and its potential is every bit as exciting as those Star Trek examples," said a blog post from Gurdeep Pall, vice president of Skype and Lync at Microsoft.

"Skype Translator opens up so many possibilities to make meaningful connections in ways you never could before in education, diplomacy, multilingual families and in business."

The product, the result of years of research at Microsoft, will be available as a Windows 8 beta app before the end of 2014, Pall said.

At the California conference, the team demonstrated near-simultaneous translation between English and German.

"There have been many attempts over the years, several within Microsoft Research, to demonstrate such aspects of translating human speech," said a Microsoft Research blog post.

"But delivering something that is usable in real life, to fit the voice and utterances of many different users and the nuances of different languages—all of it built at scale to serve Skype users—has been considered a nearly impossible task."

Microsoft said project leader Arul Menezes and Microsoft colleagues "have addressed significant system and user-interface design challenges, including reducing latency and developing visual feedback so the translation system is continuously improving itself using user feedback."

"The technology is only as good as the data," Menezes says. "One big focus has been to scale up the amount and kinds of data that go into the machine-learning training of these systems."

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