  Cross-lingual dubbing of lecture videos requires the transcription of the
original audio, correction and removal of disfluencies, domain term discovery,
text-to-text translation into the target language, chunking of text using
target language rhythm, text-to-speech synthesis followed by isochronous
lipsyncing to the original video. This task becomes challenging when the source
and target languages belong to different language families, resulting in
differences in generated audio duration. This is further compounded by the
original speaker's rhythm, especially for extempore speech. This paper
describes the challenges in regenerating English lecture videos in Indian
languages semi-automatically. A prototype is developed for dubbing lectures
into 9 Indian languages. A mean-opinion-score (MOS) is obtained for two
languages, Hindi and Tamil, on two different courses. The output video is
compared with the original video in terms of MOS (1-5) and lip synchronisation
with scores of 4.09 and 3.74, respectively. The human effort also reduces by
75%.
