摘要： AI and machine learning algorithms capable of reading lips from videos aren’t anything out of the ordinary, in truth. Back in 2016, researchers from Google and the University of Oxford detailed a system that could annotate video footage with 46.8% accuracy, outperforming a professional human lip-reader’s 12.4% accuracy. But even state-of-the-art systems struggle to overcome ambiguities in lip movements, preventing their performance from surpassing that of audio-based speech recognition.
AI and machine learning algorithms capable of reading lips from videos aren’t anything out of the ordinary, in truth. Back in 2016, researchers from Google and the University of Oxford detailed a system that could annotate video footage with 46.8% accuracy, outperforming a professional human lip-reader’s 12.4% accuracy. But even state-of-the-art systems struggle to overcome ambiguities in lip movements, preventing their performance from surpassing that of audio-based speech recognition.
In pursuit of a more performant system, researchers at Alibaba, Zhejiang University, and the Stevens Institute of Technology devised a method dubbed Lip by Speech (LIBS), which uses features extracted from speech recognizers to serve as complementary clues. They say it manages industry-leading accuracy on two benchmarks, besting the baseline by a margin of 7.66% and 2.75% in character error rate.
LIBS and other solutions like it could help those hard of hearing to follow videos that lack subtitles. It’s estimated that 466 million people in the world suffer from disabling hearing loss, or about 5% of the world’s population. By 2050, the number could rise to over 900 million, according to the World Health Organization.
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