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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM45156

The DUB Master: A Deep-Learning Pipeline for Automated, Emotion-Aware Video Dubbing

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM45156

Volume:

9 |

4 |

Apr 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

KM

Ketan Mande

DL

Darshan Londhe

HM

Harsh Mishra

MP

Mehul Parmar

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Abstract

The demand for multilingual video con- tent has soared, yet manual dubbing remains costly ($40–$100/min) and slow (3–5 days per 5-min clip). The DUB Master integrates Whisper (WER 8.4%), MarianMT (BLEU 37.5), Coqui TTS (MOS 4.2/5), and MFA (sync RMS 85 ms) into one seamless pipeline, reducing costs by 90% and turnaround by 80%. We validate on a 15-hour, 12-video benchmark with 12 native raters and conduct ablation studies to quantify each module’s contribution. Deployment pilot with VidLocal demonstrated consistent performance in live settings. In contrast to commercial dubbing tools, our system offers full transparency, modular control, and the ability to be fine-tuned on custom data. The DUB Master prioritizes cultural relevance, emotional nuance, and speaker consis- tency, which are often lost in traditional or monolingual pipelines. Our integration of emotion detection into both the translation and TTS modules ensures that output not only sounds natural but also aligns contextually with on-screen expressions and narrative tone.

KEYWORDS

Automated Dubbing Speech Recogni- tion Neural Machine Translation Voice Cloning Lip Synchronization AI Pipeline

Cite This Article

Ketan Mande, Darshan Londhe, Harsh Mishra, Mehul Parmar, "The DUB Master: A Deep-Learning Pipeline for Automated, Emotion-Aware Video Dubbing," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 4, Apr 2025.

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Publication Date

4/19/2025

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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