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Machine translation systems have made it remarkably simple to ask for directions in a foreign country. Sometimes, the systems can make amusing and innocent errors, but overall, they achieve coherent communication, at least for short exchanges only a sentence or two long. In the case of a presentation that can extend past an hour, such as an academic lecture, they are far less robust.

"NAIST has 20 percent foreign students and, while the number of English classes is expanding, the options these students have are limited by their Japanese ability," explains NAIST Professor Satoshi Nakamura, who led the study.

Nakamura's research group acquired 46.5 hours of archived lecture videos from NAIST with their transcriptions and English translations, and developed a deep learning-based system to transcribe Japanese lecture speech and subsequently translate it into English. While watching the videos, users see subtitles in Japanese and English that match the lecturer's speech.

One might expect the ideal output would be simultaneous translations that could be done with live presentations. However, live translations limit the processing time and thus the accuracy. "Because we are putting videos with subtitles in the archives, we found better translations by creating subtitles with a longer processing time," he says.

The archived footage used for the evaluation consisted of lectures from robotics, speech processing and software engineering. Interestingly, the word error rate in speech recognition correlated to disfluency in the lecturers' speech. Another factor from the different error rates was the length of time speaking without pause. The corpus used for the training was still insufficient and should be developed more for further improvements.

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