mlsanityData to 14 Oct 2026

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

arXiv 2206.13756 ↗First posted 28 Jun 2022cs.CL cs.AIcited by 6

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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.

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

11 of 19 references.

References · 11

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  3. 2019fairseq: A Fast, Extensible Toolkit for Sequence Modelingcited by 1,488
  4. 2020ESPnet-ST: All-in-One Speech Translation Toolkitcited by 82
  5. 2020Curriculum Pre-training for End-to-End Speech Translationcited by 57
  6. 2021Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Taskcited by 54
  7. 2021Learning Shared Semantic Space for Speech-to-Text Translationcited by 49
  8. 2021End-to-end Speech Translation via Cross-modal Progressive Trainingcited by 47
  9. 2018Augmenting Librispeech with French Translations: A Multimodal Corpus for Direct Speech Translation Evaluationcited by 34
  10. 2020NeurST: Neural Speech Translation Toolkitcited by 23
  11. 2021Speechformer: Reducing Information Loss in Direct Speech Translationcited by 15

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 4; 2025: 1.

6 papers.

  1. 2024Dynamic Multi-Reward Weighting for Multi-Style Controllable Generationcited by 13
  2. 2022Don't Discard Fixed-Window Audio Segmentation in Speech-to-Text Translationcited by 8
  3. 2025How to Evaluate Speech Translation with Source-Aware Neural MT Metricscited by 3
  4. 2024SSP: Self-Supervised Prompting for Cross-Lingual Transfer to Low-Resource Languages using Large Language Modelscited by 1
  5. 2024CMU's IWSLT 2024 Simultaneous Speech Translation Systemcited by 1
  6. 2024Decoding Multilingual Topic Dynamics and Trend Identification through ARIMA Time Series Analysis on Social Networks: A Novel Data Translation Framework Enhanced by LDA/HDP Modelscited by 0