# Segmenting Subtitles for Correcting ASR Segmentation Errors

arXiv: https://arxiv.org/abs/2104.07868 · HTML: https://mlsanity.com/paper/2104.07868/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 48 references that are in the map.
- First posted on arXiv 16 Apr 2021.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [Marian: Fast Neural Machine Translation in C++](https://mlsanity.com/paper/1804.00344/) (2018)
- [Sequence-to-Sequence Models Can Directly Translate Foreign Speech](https://mlsanity.com/paper/1703.08581/) (2017)
- [On the Impact of Various Types of Noise on Neural Machine Translation](https://mlsanity.com/paper/1805.12282/) (2018)
- [End-to-End Speech-Translation with Knowledge Distillation: FBK@IWSLT2020](https://mlsanity.com/paper/2006.02965/) (2020)
- [Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation](https://mlsanity.com/paper/1904.07209/) (2019)
- [Bi-Directional Neural Machine Translation with Synthetic Parallel Data](https://mlsanity.com/paper/1805.11213/) (2018)
- [Subtitles to Segmentation: Improving Low-Resource Speech-to-Text Translation Pipelines](https://mlsanity.com/paper/2010.09693/) (2020)

## Built on by (most cited first, 3 of 3)

- [Speech Segmentation Optimization using Segmented Bilingual Speech Corpus for End-to-end Speech Translation](https://mlsanity.com/paper/2203.15479/) (2022)
- [Lightweight Audio Segmentation for Long-form Speech Translation](https://mlsanity.com/paper/2406.10549/) (2024)
- [When Misinformation Speaks and Converses: Rethinking Fact-Checking in Audio Platforms](https://mlsanity.com/paper/2604.16767/) (2026)

## Abstract (arXiv)

Typical ASR systems segment the input audio into utterances using purely acoustic information, which may not resemble the sentence-like units that are expected by conventional machine translation (MT) systems for Spoken Language Translation. In this work, we propose a model for correcting the acoustic segmentation of ASR models for low-resource languages to improve performance on downstream tasks. We propose the use of subtitles as a proxy dataset for correcting ASR acoustic segmentation, creating synthetic acoustic utterances by modeling common error modes. We train a neural tagging model for correcting ASR acoustic segmentation and show that it improves downstream performance on MT and audio-document cross-language information retrieval (CLIR).