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DNN technology had come a long way, and scientists at MSR and elsewhere had by this point been able to develop sophisticated machine learning models via DNNs that performed more like neurons in the human brain than traditional computers. “Then we finally hit a tipping point.” MSR had never fully walked away from DNN research, and when a group of machine translation researchers began actively pursuing them as a means of creating faster, more efficient speech recognition engines, they experienced the breakthrough they’d long sought.
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“There was a full 10-year period in which we were working really hard and discovering new things every day, but the quality of our system wasn’t improving,” Lee says.
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Designed more like the human brain than a classical computerįor something like a decade, machine translation performance stagnated.
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The real breakthrough for real-time speech-to-speech translation came around in 2009, when a group at MSR decided to return to deep neural network research in an effort to enhance speech recognition and synthesis-the turning of spoken words into text and vice versa. And anyhow, translation is no longer the hardest part of the equation. This kind of probabilistic, statistical matching allows the system to get smarter over time, but it doesn’t really represent a breakthrough in machine learning or translation (though MSR researchers would point out that they’ve built some pretty sophisticated and unique syntax parsing algorithms into their engine). With enough use, it figures out that an equivalent phrase, “the drop that tipped the bucket,” will likely sound more familiar to a German speaker. Over time the system builds confidence in certain results, reducing errors. To translate an English phrase like “the straw that broke the camel’s back” into, say, German, the system looks for probabilistic matches, selecting the best solution from a number of candidate phrases based on what it thinks is most likely to be correct. But if I say ‘oh, that fumble was the straw that broke the camel’s back,’ if you do a word-for-word translation into another language it probably wouldn’t make much sense.” “If I say ‘I like ice cream,’ you know that it probably means what it means. “It’s combination of understanding the language-syntax and structure and meaning-but also a statistical matching process,” he says. There’s more than one way to train a computer on language, says MSR Corporate Vice President Peter Lee, but there’s also more than one way for human language to trip up a computer. They would have to teach the machine, and the machine would have to learn. To create a working speech-to-speech translation technology, MSR researchers knew they would have to teach their system to not only translate one word to the same word in another language based on a standard set of rules, but to understand the meaning of words and sentences. Half a beat after you stop speaking, an audio translation plays Their lofty goal: To make it possible for every human on Earth to communicate with any other human on Earth To do so, Microsoft Research (MSR) had to solve some major machine learning problems while pushing technologies like deep neural networks into new territory. Department of Defense-have not yet been able to do. The product of more than a decade of dedicated research and development by Microsoft Research (Microsoft acquired Skype in 2011), Skype Translator does what several other Silicon Valley icons-not to mention the U.S. Skype plans to incrementally add support for more than 40 languages, promising nothing short of a universal translator for desktops and mobile devices. The new function, called Skype Translator, translates voice calls between different languages in realtime, turning English to Spanish and Spanish back into English on the fly. Earlier this week, roughly 50,000 Skype users woke up to a new way of communicating over the Web-based phone- and video-calling platform, a feature that could’ve been pulled straight out of Star Trek.