# Is 42 the Answer to Everything in Subtitling-oriented Speech Translation?

arXiv: https://arxiv.org/abs/2006.01080 · HTML: https://mlsanity.com/paper/2006.01080/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2020.

## Built on by (most cited first, 21 of 21)

- [Isometric MT: Neural Machine Translation for Automatic Dubbing](https://mlsanity.com/paper/2112.08682/) (2021)
- [Beyond Voice Activity Detection: Hybrid Audio Segmentation for Direct Speech Translation](https://mlsanity.com/paper/2104.11710/) (2021)
- [How "Real" is Your Real-Time Simultaneous Speech-to-Text Translation System?](https://mlsanity.com/paper/2412.18495/) (2024)
- [Isochrony-Aware Neural Machine Translation for Automatic Dubbing](https://mlsanity.com/paper/2112.08548/) (2021)
- [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)
- [Between Flexibility and Consistency: Joint Generation of Captions and Subtitles](https://mlsanity.com/paper/2107.06246/) (2021)
- [Direct Speech Translation for Automatic Subtitling](https://mlsanity.com/paper/2209.13192/) (2022)
- [Direct Models for Simultaneous Translation and Automatic Subtitling: FBK@IWSLT2023](https://mlsanity.com/paper/2309.15554/) (2023)
- [Joint Generation of Captions and Subtitles with Dual Decoding](https://mlsanity.com/paper/2205.06522/) (2022)
- [Dodging the Data Bottleneck: Automatic Subtitling with Automatically Segmented ST Corpora](https://mlsanity.com/paper/2209.10608/) (2022)
- [Lyrics Transcription for Humans: A Readability-Aware Benchmark](https://mlsanity.com/paper/2408.06370/) (2024)
- [Evaluating Subtitle Segmentation for End-to-end Generation Systems](https://mlsanity.com/paper/2205.09360/) (2022)
- [Automating Easy Read Text Segmentation](https://mlsanity.com/paper/2406.11464/) (2024)
- [Jam-ALT: A Formatting-Aware Lyrics Transcription Benchmark](https://mlsanity.com/paper/2311.13987/) (2023)
- [Prompting LLMs: Length Control for Isometric Machine Translation](https://mlsanity.com/paper/2506.04855/) (2025)
- [SBAAM! Eliminating Transcript Dependency in Automatic Subtitling](https://mlsanity.com/paper/2405.10741/) (2024)
- [IsoChronoMeter: A simple and effective isochronic translation evaluation metric](https://mlsanity.com/paper/2410.11127/) (2024)
- [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)
- [Evaluating the Efficacy of Length-Controllable Machine Translation](https://mlsanity.com/paper/2305.02300/) (2023)

## Abstract (arXiv)

Subtitling is becoming increasingly important for disseminating information, given the enormous amounts of audiovisual content becoming available daily. Although Neural Machine Translation (NMT) can speed up the process of translating audiovisual content, large manual effort is still required for transcribing the source language, and for spotting and segmenting the text into proper subtitles. Creating proper subtitles in terms of timing and segmentation highly depends on information present in the audio (utterance duration, natural pauses). In this work, we explore two methods for applying Speech Translation (ST) to subtitling: a) a direct end-to-end and b) a classical cascade approach. We discuss the benefit of having access to the source language speech for improving the conformity of the generated subtitles to the spatial and temporal subtitling constraints and show that length is not the answer to everything in the case of subtitling-oriented ST.