# Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech Translation

arXiv: https://arxiv.org/abs/2406.17363 · HTML: https://mlsanity.com/paper/2406.17363/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 26 references that are in the map.
- First posted on arXiv 25 Jun 2024.

## Builds on (most cited first, 15 of 15)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [SeamlessM4T: Massively Multilingual & Multimodal Machine Translation](https://mlsanity.com/paper/2308.11596/) (2023)
- [The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Text](https://mlsanity.com/paper/2311.09807/) (2023)
- [Survey of Low-Resource Machine Translation](https://mlsanity.com/paper/2109.00486/) (2021)
- [SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages](https://mlsanity.com/paper/2406.10118/) (2024)
- [Domain, Translationese and Noise in Synthetic Data for Neural Machine Translation](https://mlsanity.com/paper/1911.03362/) (2019)
- [gaBERT -- an Irish Language Model](https://mlsanity.com/paper/2107.12930/) (2021)
- [Domain-Specific Text Generation for Machine Translation](https://mlsanity.com/paper/2208.05909/) (2022)
- [Transformers for Low-Resource Languages: Is F'eidir Linn!](https://mlsanity.com/paper/2403.01985/) (2024)
- [Adaptation of Machine Translation Models with Back-translated Data using Transductive Data Selection Methods](https://mlsanity.com/paper/1906.07808/) (2019)

## Built on by (most cited first, 9 of 9)

- [KIT's Low-resource Speech Translation Systems for IWSLT2025: System Enhancement with Synthetic Data and Model Regularization](https://mlsanity.com/paper/2505.19679/) (2025)
- [Synthetic Voice Data for Automatic Speech Recognition in African Languages](https://mlsanity.com/paper/2507.17578/) (2025)
- [Cost Analysis of Human-corrected Transcription for Predominately Oral Languages](https://mlsanity.com/paper/2510.12781/) (2025)
- [Kunnafonidilaw ka Cadeau: an ASR dataset of present-day Bambara](https://mlsanity.com/paper/2512.19400/) (2025)
- [SpeechT: Findings of the First Mentorship in Speech Translation](https://mlsanity.com/paper/2502.12050/) (2025)
- [Bemba Speech Translation: Exploring a Low-Resource African Language](https://mlsanity.com/paper/2505.02518/) (2025)
- [GMU Systems for the IWSLT 2025 Low-Resource Speech Translation Shared Task](https://mlsanity.com/paper/2505.21781/) (2025)
- [It's Not a Walk in the Park! Challenges of Idiom Translation in Speech-to-text Systems](https://mlsanity.com/paper/2506.02995/) (2025)
- [BlasBench: An Open Benchmark for Irish Speech Recognition](https://mlsanity.com/paper/2604.10736/) (2026)

## Abstract (arXiv)

This paper describes our system submission to the International Conference on Spoken Language Translation (IWSLT 2024) for Irish-to-English speech translation. We built end-to-end systems based on Whisper, and employed a number of data augmentation techniques, such as speech back-translation and noise augmentation. We investigate the effect of using synthetic audio data and discuss several methods for enriching signal diversity.