They accomplish this in several major steps. "Given a video of a person speaking, we have two major information streams to translate: the visual and the speech information," he explains. "By doing so, the translation system becomes holistic, and as shown by our human evaluations in this paper, significantly improves the user experience in creating and consuming translated audio-visual content."įace-to-Face Translation requires a number of complex feats. "Thus, we build upon the speech-to-speech translation systems and propose a pipeline that can take a video of a person speaking in a source language and output a video of the same speaker speaking in a target language such that the voice style and lip movements matches the target language speech," Prajwal says. As a result, the translated speech when overlaid on the video, the lip movements would be out of sync with the audio. "Current translation systems can only generate a translated speech output or textual subtitles for such video content.
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"Today, the internet is filled with talking face videos: YouTube (300 hours uploaded per day), online lectures, video conferencing, movies, TV shows and so on," Prajwal, who goes by his given name, writes. He's the lead author of the paper, along with his colleague Rudrabha Mukhopadhyay. "Communicating effectively across language barriers has always been a major aspiration for humans all over the world," Prajwal K.R., a graduate student in computer science at the International Institute of Information Technology in Hyderabad, India, explains via email.
A moment later, you'll see her uttering the same quote in Hindi - with her lips moving, as if she actually spoke that language.
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