# On the Impact of Noises in Crowd-Sourced Data for Speech Translation

arXiv: https://arxiv.org/abs/2206.13756 · HTML: https://mlsanity.com/paper/2206.13756/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 28 Jun 2022.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [ESPnet-ST: All-in-One Speech Translation Toolkit](https://mlsanity.com/paper/2004.10234/) (2020)
- [Curriculum Pre-training for End-to-End Speech Translation](https://mlsanity.com/paper/2004.10093/) (2020)
- [Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task](https://mlsanity.com/paper/2107.05782/) (2021)
- [Learning Shared Semantic Space for Speech-to-Text Translation](https://mlsanity.com/paper/2105.03095/) (2021)
- [End-to-end Speech Translation via Cross-modal Progressive Training](https://mlsanity.com/paper/2104.10380/) (2021)
- [Augmenting Librispeech with French Translations: A Multimodal Corpus for Direct Speech Translation Evaluation](https://mlsanity.com/paper/1802.03142/) (2018)
- [NeurST: Neural Speech Translation Toolkit](https://mlsanity.com/paper/2012.10018/) (2020)
- [Speechformer: Reducing Information Loss in Direct Speech Translation](https://mlsanity.com/paper/2109.04574/) (2021)

## Built on by (most cited first, 6 of 6)

- [Dynamic Multi-Reward Weighting for Multi-Style Controllable Generation](https://mlsanity.com/paper/2402.14146/) (2024)
- [Don't Discard Fixed-Window Audio Segmentation in Speech-to-Text Translation](https://mlsanity.com/paper/2210.13363/) (2022)
- [How to Evaluate Speech Translation with Source-Aware Neural MT Metrics](https://mlsanity.com/paper/2511.03295/) (2025)
- [SSP: Self-Supervised Prompting for Cross-Lingual Transfer to Low-Resource Languages using Large Language Models](https://mlsanity.com/paper/2406.18880/) (2024)
- [CMU's IWSLT 2024 Simultaneous Speech Translation System](https://mlsanity.com/paper/2408.07452/) (2024)
- [Decoding Multilingual Topic Dynamics and Trend Identification through ARIMA Time Series Analysis on Social Networks: A Novel Data Translation Framework Enhanced by LDA/HDP Models](https://mlsanity.com/paper/2403.15445/) (2024)

## Abstract (arXiv)

Training speech translation (ST) models requires large and high-quality datasets. MuST-C is one of the most widely used ST benchmark datasets. It contains around 400 hours of speech-transcript-translation data for each of the eight translation directions. This dataset passes several quality-control filters during creation. However, we find that MuST-C still suffers from three major quality issues: audio-text misalignment, inaccurate translation, and unnecessary speaker's name. What are the impacts of these data quality issues for model development and evaluation? In this paper, we propose an automatic method to fix or filter the above quality issues, using English-German (En-De) translation as an example. Our experiments show that ST models perform better on clean test sets, and the rank of proposed models remains consistent across different test sets. Besides, simply removing misaligned data points from the training set does not lead to a better ST model.