# ELITR Non-Native Speech Translation at IWSLT 2020

arXiv: https://arxiv.org/abs/2006.03331 · HTML: https://mlsanity.com/paper/2006.03331/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2020.

## Built on by (most cited first, 5 of 5)

- [AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End Dialogue with Pre-Trained Language Models](https://mlsanity.com/paper/2102.05126/) (2021)
- [How "Real" is Your Real-Time Simultaneous Speech-to-Text Translation System?](https://mlsanity.com/paper/2412.18495/) (2024)
- [Variational Latent-State GPT for Semi-Supervised Task-Oriented Dialog Systems](https://mlsanity.com/paper/2109.04314/) (2021)
- [Backtranslation Feedback Improves User Confidence in MT, Not Quality](https://mlsanity.com/paper/2104.05688/) (2021)
- [Lost in Interpreting: Speech Translation from Source or Interpreter?](https://mlsanity.com/paper/2106.09343/) (2021)

## Abstract (arXiv)

This paper is an ELITR system submission for the non-native speech translation task at IWSLT 2020. We describe systems for offline ASR, real-time ASR, and our cascaded approach to offline SLT and real-time SLT. We select our primary candidates from a pool of pre-existing systems, develop a new end-to-end general ASR system, and a hybrid ASR trained on non-native speech. The provided small validation set prevents us from carrying out a complex validation, but we submit all the unselected candidates for contrastive evaluation on the test set.