# VideoDubber: Machine Translation with Speech-Aware Length Control for Video Dubbing

arXiv: https://arxiv.org/abs/2211.16934 · HTML: https://mlsanity.com/paper/2211.16934/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 30 Nov 2022.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality](https://mlsanity.com/paper/2205.04421/) (2022)
- [CVSS Corpus and Massively Multilingual Speech-to-Speech Translation](https://mlsanity.com/paper/2201.03713/) (2022)
- [Positional Encoding to Control Output Sequence Length](https://mlsanity.com/paper/1904.07418/) (2019)
- [AdaSpeech 4: Adaptive Text to Speech in Zero-Shot Scenarios](https://mlsanity.com/paper/2204.00436/) (2022)
- [Controlling the Output Length of Neural Machine Translation](https://mlsanity.com/paper/1910.10408/) (2019)
- [From Speech-to-Speech Translation to Automatic Dubbing](https://mlsanity.com/paper/2001.06785/) (2020)
- [Machine Translation Verbosity Control for Automatic Dubbing](https://mlsanity.com/paper/2110.03847/) (2021)
- [Isometric MT: Neural Machine Translation for Automatic Dubbing](https://mlsanity.com/paper/2112.08682/) (2021)
- [Isochrony-Aware Neural Machine Translation for Automatic Dubbing](https://mlsanity.com/paper/2112.08548/) (2021)
- [Prosodic Phrase Alignment for Machine Dubbing](https://mlsanity.com/paper/1908.07226/) (2019)
- [Prosodic Alignment for off-screen automatic dubbing](https://mlsanity.com/paper/2204.02530/) (2022)

## Built on by (most cited first, 20 of 20)

- [TransVIP: Speech to Speech Translation System with Voice and Isochrony Preservation](https://mlsanity.com/paper/2405.17809/) (2024)
- [UniSS: Unified Expressive Speech-to-Speech Translation with Your Voice](https://mlsanity.com/paper/2509.21144/) (2025)
- [ANIM-400K: A Large-Scale Dataset for Automated End-To-End Dubbing of Video](https://mlsanity.com/paper/2401.05314/) (2024)
- [Jointly Optimizing Translations and Speech Timing to Improve Isochrony in Automatic Dubbing](https://mlsanity.com/paper/2302.12979/) (2023)
- [SegTalker: Segmentation-based Talking Face Generation with Mask-guided Local Editing](https://mlsanity.com/paper/2409.03605/) (2024)
- [Isometric Neural Machine Translation using Phoneme Count Ratio Reward-based Reinforcement Learning](https://mlsanity.com/paper/2403.15469/) (2024)
- [Improving Isochronous Machine Translation with Target Factors and Auxiliary Counters](https://mlsanity.com/paper/2305.13204/) (2023)
- [Fine-grained Video Dubbing Duration Alignment with Segment Supervised Preference Optimization](https://mlsanity.com/paper/2508.08550/) (2025)
- [EmoReg: Directional Latent Vector Modeling for Emotional Intensity Regularization in Diffusion-based Voice Conversion](https://mlsanity.com/paper/2412.20359/) (2024)
- [Hermes the Polyglot: A Unified Framework to Enhance Expressiveness for Multimodal Interlingual Subtitling](https://mlsanity.com/paper/2602.00597/) (2026)
- [IsoChronoMeter: A simple and effective isochronic translation evaluation metric](https://mlsanity.com/paper/2410.11127/) (2024)
- [Dub-S2ST: Textless Speech-to-Speech Translation for Seamless Dubbing](https://mlsanity.com/paper/2505.20899/) (2025)
- [Length Aware Speech Translation for Video Dubbing](https://mlsanity.com/paper/2506.00740/) (2025)
- [HOMURA: Taming the Sand-Glass for Time-Constrained LLM Translation via Reinforcement Learning](https://mlsanity.com/paper/2601.10187/) (2026)
- [From Utterance to Vividity: Training Expressive Subtitle Translation LLM via Adaptive Local Preference Optimization](https://mlsanity.com/paper/2602.01068/) (2026)
- [OpenSTBench: Beyond Semantic Evaluation for Speech Translation](https://mlsanity.com/paper/2605.30792/) (2026)
- [STEB: A Speech-to-Speech Translation Expressiveness Benchmark for Evaluating Beyond Translation Fidelity](https://mlsanity.com/paper/2606.25529/) (2026)
- [CineSRD: Leveraging Visual, Acoustic, and Linguistic Cues for Open-World Visual Media Speaker Diarization](https://mlsanity.com/paper/2603.16966/) (2026)
- [PS-TTS: Phonetic Synchronization in Text-to-Speech for Achieving Natural Automated Dubbing](https://mlsanity.com/paper/2604.09111/) (2026)
- [DuraS2ST: Chain-of-Thought and Reinforcement Learning for Duration-Aligned Speech-to-Speech Translation](https://mlsanity.com/paper/2609.33742/) (2026)

## Abstract (arXiv)

Video dubbing aims to translate the original speech in a film or television program into the speech in a target language, which can be achieved with a cascaded system consisting of speech recognition, machine translation and speech synthesis. To ensure the translated speech to be well aligned with the corresponding video, the length/duration of the translated speech should be as close as possible to that of the original speech, which requires strict length control. Previous works usually control the number of words or characters generated by the machine translation model to be similar to the source sentence, without considering the isochronicity of speech as the speech duration of words/characters in different languages varies. In this paper, we propose a machine translation system tailored for the task of video dubbing, which directly considers the speech duration of each token in translation, to match the length of source and target speech. Specifically, we control the speech length of generated sentence by guiding the prediction of each word with the duration information, including the speech duration of itself as well as how much duration is left for the remaining words. We design experiments on four language directions (German -> English, Spanish -> English, Chinese <-> English), and the results show that the proposed method achieves better length control ability on the generated speech than baseline methods. To make up the lack of real-world datasets, we also construct a real-world test set collected from films to provide comprehensive evaluations on the video dubbing task.