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AI Has Beaten Humans at Lip-reading

2019.01.16|
AI

Lip-reading is notoriously difficult, depending as much on context and knowledge of language as it does on visual clues. But researchers are showing that machine learning can be used to discern speech from silent video clips more effectively than professional lip-readers can.

In one project, a team from the University of Oxford’s Department of Computer Science has developed a new artificial-intelligence system called LipNet. As Quartz reported, its system was built on a data set known as GRID, which is made up of well-lit, face-forward clips of people reading three-second sentences. Each sentence is based on a string of words that follow the same pattern.

The team used that data set to train a neural network, similar to the kind often used to perform speech recognition. In this case, though, the neural network identifies variations in mouth shape over time, learning to link that information to an explanation of what’s being said. The AI doesn’t analyze the footage in snatches but considers the whole thing, enabling it to gain an understanding of context from the sentence being analyzed. That’s important, because there are fewer mouth shapes than there are sounds produced by the human voice.

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