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

• URL: http://arxiv.org/abs/2211.01338v1
• Date: Tue, 1 Nov 2022 07:06:29 GMT
• Title: Technology Pipeline for Large Scale Cross-Lingual Dubbing of Lecture Videos into Multiple Indian Languages
• Authors: Anusha Prakash, Arun Kumar, Ashish Seth, Bhagyashree Mukherjee, Ishika Gupta, Jom Kuriakose, Jordan Fernandes, K V Vikram, Mano Ranjith Kumar M, Metilda Sagaya Mary, Mohammad Wajahat, Mohana N, Mudit Batra, Navina K, Nihal John George, Nithya Ravi, Pruthwik Mishra, Sudhanshu Srivastava, Vasista Sai Lodagala, Vandan Mujadia, Kada Sai Venkata Vineeth, Vrunda Sukhadia, Dipti Sharma, Hema Murthy, Pushpak Bhattacharya, S Umesh, Rajeev Sangal
• Abstract summary: Cross-lingual dubbing of lecture videos requires the transcription of the original audio, correction and removal of disfluencies. This paper describes the challenges in regenerating English lecture videos in Indian languages semi-automatically.
• Score: 5.17905382659474
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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