# V-SAT: Video Subtitle Annotation Tool

arXiv: https://arxiv.org/abs/2510.24180 · HTML: https://mlsanity.com/paper/2510.24180/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 28 Oct 2025.

## Builds on (most cited first, 7 of 7)

- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Speech Translation and the End-to-End Promise: Taking Stock of Where We Are](https://mlsanity.com/paper/2004.06358/) (2020)
- [MuST-Cinema: a Speech-to-Subtitles corpus](https://mlsanity.com/paper/2002.10829/) (2020)
- [SubER: A Metric for Automatic Evaluation of Subtitle Quality](https://mlsanity.com/paper/2205.05805/) (2022)
- [Simultaneous Speech Translation for Live Subtitling: from Delay to Display](https://mlsanity.com/paper/2107.08807/) (2021)
- [Direct Speech Translation for Automatic Subtitling](https://mlsanity.com/paper/2209.13192/) (2022)
- [Dialogue Understandability: Why are we streaming movies with subtitles?](https://mlsanity.com/paper/2403.15336/) (2024)

## Abstract (arXiv)

The surge of audiovisual content on streaming platforms and social media has heightened the demand for accurate and accessible subtitles. However, existing subtitle generation methods primarily speech-based transcription or OCR-based extraction suffer from several shortcomings, including poor synchronization, incorrect or harmful text, inconsistent formatting, inappropriate reading speeds, and the inability to adapt to dynamic audio-visual contexts. Current approaches often address isolated issues, leaving post-editing as a labor-intensive and time-consuming process. In this paper, we introduce V-SAT (Video Subtitle Annotation Tool), a unified framework that automatically detects and corrects a wide range of subtitle quality issues. By combining Large Language Models(LLMs), Vision-Language Models (VLMs), Image Processing, and Automatic Speech Recognition (ASR), V-SAT leverages contextual cues from both audio and video. Subtitle quality improved, with the SUBER score reduced from 9.6 to 3.54 after resolving all language mode issues and F1-scores of ~0.80 for image mode issues. Human-in-the-loop validation ensures high-quality results, providing the first comprehensive solution for robust subtitle annotation.