# Robust Neural Machine Translation for Clean and Noisy Speech Transcripts

arXiv: https://arxiv.org/abs/1910.10238 · HTML: https://mlsanity.com/paper/1910.10238/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2019.

## Built on by (most cited first, 13 of 13)

- [Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference?](https://mlsanity.com/paper/2106.01045/) (2021)
- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [An Approach to Improve Robustness of NLP Systems against ASR Errors](https://mlsanity.com/paper/2103.13610/) (2021)
- [Measuring the Effect of Transcription Noise on Downstream Language Understanding Tasks](https://mlsanity.com/paper/2502.13645/) (2025)
- [Prosodic Alignment for off-screen automatic dubbing](https://mlsanity.com/paper/2204.02530/) (2022)
- [Improving Cascaded Unsupervised Speech Translation with Denoising Back-translation](https://mlsanity.com/paper/2305.07455/) (2023)
- [Towards Building Large Scale Datasets and State-of-the-Art Automatic Speech Translation Systems for 14 Indian Languages](https://mlsanity.com/paper/2411.04699/) (2024)
- [When End-to-End is Overkill: Rethinking Cascaded Speech-to-Text Translation](https://mlsanity.com/paper/2502.00377/) (2025)
- [Improving speech translation by fusing speech and text](https://mlsanity.com/paper/2305.14042/) (2023)
- [Robustness of Multi-Source MT to Transcription Errors](https://mlsanity.com/paper/2305.16894/) (2023)
- [Interventional Speech Noise Injection for ASR Generalizable Spoken Language Understanding](https://mlsanity.com/paper/2410.15609/) (2024)
- [Leveraging Sentence-oriented Augmentation and Transformer-Based Architecture for Vietnamese-Bahnaric Translation](https://mlsanity.com/paper/2601.19124/) (2026)
- [PiDA: Phonetically-Informed Data Augmentation for Robust Vietnamese Speech Translation](https://mlsanity.com/paper/2606.12911/) (2026)

## Abstract (arXiv)

Neural machine translation models have shown to achieve high quality when trained and fed with well structured and punctuated input texts. Unfortunately, the latter condition is not met in spoken language translation, where the input is generated by an automatic speech recognition (ASR) system. In this paper, we study how to adapt a strong NMT system to make it robust to typical ASR errors. As in our application scenarios transcripts might be post-edited by human experts, we propose adaptation strategies to train a single system that can translate either clean or noisy input with no supervision on the input type. Our experimental results on a public speech translation data set show that adapting a model on a significant amount of parallel data including ASR transcripts is beneficial with test data of the same type, but produces a small degradation when translating clean text. Adapting on both clean and noisy variants of the same data leads to the best results on both input types.