Technology Pipeline for Large Scale Cross-Lingual Dubbing of Lecture Videos into Multiple Indian Languages

Prakash, Anusha, Kumar, Arun, Seth, Ashish, Mukherjee, Bhagyashree, Gupta, Ishika, Kuriakose, Jom, Fernandes, Jordan, Vikram, K V, M, Mano Ranjith Kumar, Mary, Metilda Sagaya, Wajahat, Mohammad, N, Mohana, Batra, Mudit, K, Navina, George, Nihal John, Ravi, Nithya, Mishra, Pruthwik, Srivastava, Sudhanshu, Lodagala, Vasista Sai, Mujadia, Vandan, Vineeth, Kada Sai Venkata, Sukhadia, Vrunda, Sharma, Dipti, Murthy, Hema, Bhattacharya, Pushpak, Umesh, S, Sangal, Rajeev

arXiv.org Artificial Intelligence 

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%.

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