# DubWise: Video-Guided Speech Duration Control in Multimodal LLM-based Text-to-Speech for Dubbing

arXiv: https://arxiv.org/abs/2406.08802 · HTML: https://mlsanity.com/paper/2406.08802/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2024.

## Built on by (most cited first, 10 of 10)

- [IndexTTS2: A Breakthrough in Emotionally Expressive and Duration-Controlled Auto-Regressive Zero-Shot Text-to-Speech](https://mlsanity.com/paper/2506.21619/) (2025)
- [FlowDubber: Movie Dubbing with LLM-based Semantic-aware Learning and Flow Matching based Voice Enhancing](https://mlsanity.com/paper/2505.01263/) (2025)
- [FolAI: Synchronized Foley Sound Generation with Semantic and Temporal Alignment](https://mlsanity.com/paper/2412.15023/) (2024)
- [Towards Expressive Video Dubbing with Multiscale Multimodal Context Interaction](https://mlsanity.com/paper/2412.18748/) (2024)
- [EmoReg: Directional Latent Vector Modeling for Emotional Intensity Regularization in Diffusion-based Voice Conversion](https://mlsanity.com/paper/2412.20359/) (2024)
- [Dub-S2ST: Textless Speech-to-Speech Translation for Seamless Dubbing](https://mlsanity.com/paper/2505.20899/) (2025)
- [CrossAccent-TTS: Cross-Lingual Accent-Intensity Controllable Text-to-Speech via Disentangled Speaker and Accent Representations](https://mlsanity.com/paper/2606.25403/) (2026)
- [Towards Film-Making Production Dialogue, Narration, Monologue Adaptive Moving Dubbing Benchmarks](https://mlsanity.com/paper/2505.01450/) (2025)
- [Gesture2Speech: How Far Can Hand Movements Shape Expressive Speech?](https://mlsanity.com/paper/2603.19831/) (2026)
- [Not Quite My Tempo: Voice Activity-aware Speech Synthesis for Lip-Synchronous Dubbing](https://mlsanity.com/paper/2609.26486/) (2026)

## Abstract (arXiv)

Audio-visual alignment after dubbing is a challenging research problem. To this end, we propose a novel method, DubWise Multi-modal Large Language Model (LLM)-based Text-to-Speech (TTS), which can control the speech duration of synthesized speech in such a way that it aligns well with the speakers lip movements given in the reference video even when the spoken text is different or in a different language. To accomplish this, we propose to utilize cross-modal attention techniques in a pre-trained GPT-based TTS. We combine linguistic tokens from text, speaker identity tokens via a voice cloning network, and video tokens via a proposed duration controller network. We demonstrate the effectiveness of our system on the Lip2Wav-Chemistry and LRS2 datasets. Also, the proposed method achieves improved lip sync and naturalness compared to the SOTAs for the same language but different text (i.e., non-parallel) and the different language, different text (i.e., cross-lingual) scenarios.