# Selective Data Augmentation for Robust Speech Translation

arXiv: https://arxiv.org/abs/2304.03169 · HTML: https://mlsanity.com/paper/2304.03169/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 22 Mar 2023.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Comparative Study on Transformer vs RNN in Speech Applications](https://mlsanity.com/paper/1909.06317/) (2019)
- [Listen and Translate: A Proof of Concept for End-to-End Speech-to-Text Translation](https://mlsanity.com/paper/1612.01744/) (2016)
- [ESPnet-ST: All-in-One Speech Translation Toolkit](https://mlsanity.com/paper/2004.10234/) (2020)
- [Speech Translation and the End-to-End Promise: Taking Stock of Where We Are](https://mlsanity.com/paper/2004.06358/) (2020)
- [The IIT Bombay English-Hindi Parallel Corpus](https://mlsanity.com/paper/1710.02855/) (2017)
- [End-to-End Speech Translation with Knowledge Distillation](https://mlsanity.com/paper/1904.08075/) (2019)
- [Augmenting Librispeech with French Translations: A Multimodal Corpus for Direct Speech Translation Evaluation](https://mlsanity.com/paper/1802.03142/) (2018)
- [One-To-Many Multilingual End-to-end Speech Translation](https://mlsanity.com/paper/1910.03320/) (2019)
- [Low-Resource Speech-to-Text Translation](https://mlsanity.com/paper/1803.09164/) (2018)

## Abstract (arXiv)

Speech translation (ST) systems translate speech in one language to text in another language. End-to-end ST systems (e2e-ST) have gained popularity over cascade systems because of their enhanced performance due to reduced latency and computational cost. Though resource intensive, e2e-ST systems have the inherent ability to retain para and non-linguistic characteristics of the speech unlike cascade systems. In this paper, we propose to use an e2e architecture for English-Hindi (en-hi) ST. We use two imperfect machine translation (MT) services to translate Libri-trans en text into hi text. While each service gives MT data individually to generate parallel ST data, we propose a data augmentation strategy of noisy MT data to aid robust ST. The main contribution of this paper is the proposal of a data augmentation strategy. We show that this results in better ST (BLEU score) compared to brute force augmentation of MT data. We observed an absolute improvement of 1.59 BLEU score with our approach.