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Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus

arXiv 2006.05754 ↗First posted 10 Jun 2020cs.CL cs.AI cs.SDcited by 57

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Abstract (arXiv)

Translating from languages without productive grammatical gender like English into gender-marked languages is a well-known difficulty for machines. This difficulty is also due to the fact that the training data on which models are built typically reflect the asymmetries of natural languages, gender bias included. Exclusively fed with textual data, machine translation is intrinsically constrained by the fact that the input sentence does not always contain clues about the gender identity of the referred human entities. But what happens with speech translation, where the input is an audio signal? Can audio provide additional information to reduce gender bias? We present the first thorough investigation of gender bias in speech translation, contributing with: i) the release of a benchmark useful for future studies, and ii) the comparison of different technologies (cascade and end-to-end) on two language directions (English-Italian/French).

Key facts. Cited by 57 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jun 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 9; 2022: 4; 2023: 14; 2024: 14; 2025: 9; 2026: 7.

57 papers.

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  1. 2021Gender Bias in Machine Translationcited by 116
  2. 2021A Survey on Gender Bias in Natural Language Processingcited by 89
  3. 2022MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translationcited by 38
  4. 2021Gender Bias Amplification During Speed-Quality Optimization in Neural Machine Translationcited by 28
  5. 2022CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formalitycited by 24
  6. 2022Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translationcited by 23
  7. 2023Gender Neutralization for an Inclusive Machine Translation: from Theoretical Foundations to Open Challengescited by 22
  8. 2021Investigating Failures of Automatic Translation in the Case of Unambiguous Gendercited by 20
  9. 2021Don't speak too fast: The impact of data bias on self-supervised speech modelscited by 20
  10. 2023Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with the GeNTE Corpuscited by 20
  11. 2023A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translationcited by 19
  12. 2023GATE: A Challenge Set for Gender-Ambiguous Translation Examplescited by 18
38 more
  1. 2021GENder-IT: An Annotated English-Italian Parallel Challenge Set for Cross-Linguistic Natural Gender Phenomenacited by 16
  2. 2023Recent Advances in Direct Speech-to-text Translationcited by 15
  3. 2024What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-centered Studycited by 15
  4. 2024The power of Prompts: Evaluating and Mitigating Gender Bias in MT with LLMscited by 12
  5. 2021How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translationcited by 11
  6. 2021Generating Gender Augmented Data for NLPcited by 11
  7. 2022What Do Compressed Multilingual Machine Translation Models Forget?cited by 9
  8. 2024Contrastive Learning for Task-Independent SpeechLLM-Pretrainingcited by 8
  9. 2024Enhancing Gender-Inclusive Machine Translation with Neomorphemes and Large Language Modelscited by 7
  10. 2025Are We Paying Attention to Her? Investigating Gender Disambiguation and Attention in Machine Translationcited by 7
  11. 2023Test Suites Task: Evaluation of Gender Fairness in MT with MuST-SHE and INEScited by 6
  12. 2024SPES: Spectrogram Perturbation for Explainable Speech-to-Text Generationcited by 6
  13. 2021Improving Gender Translation Accuracy with Filtered Self-Trainingcited by 5
  14. 2024MiTTenS: A Dataset for Evaluating Gender Mistranslationcited by 5
  15. 2025Assumed Identities: Quantifying Gender Bias in Machine Translation of Gender-Ambiguous Occupational Termscited by 5
  16. 2025Voice, Bias, and Coreference: An Interpretability Study of Gender in Speech Translationcited by 5
  17. 2023No Pitch Left Behind: Addressing Gender Unbalance in Automatic Speech Recognition through Pitch Manipulationcited by 4
  18. 2023Gender Inflected or Bias Inflicted: On Using Grammatical Gender Cues for Bias Evaluation in Machine Translationcited by 4
  19. 2024Gender Bias in Machine Translation and The Era of Large Language Modelscited by 4
  20. 2024GATE X-E : A Challenge Set for Gender-Fair Translations from Weakly-Gendered Languagescited by 4
  21. 2024MELD-ST: An Emotion-aware Speech Translation Datasetcited by 4
  22. 2025EuroGEST: Investigating gender stereotypes in multilingual language modelscited by 4
  23. 2025GAMBIT+: A Challenge Set for Evaluating Gender Bias in Machine Translation Quality Estimation Metricscited by 4
  24. 2023Gender Lost In Translation: How Bridging The Gap Between Languages Affects Gender Bias in Zero-Shot Multilingual Translationcited by 3
  25. 2024Generating Gender Alternatives in Machine Translationcited by 3
  26. 2025Different Speech Translation Models Encode and Translate Speaker Gender Differentlycited by 3
  27. 2025The Unheard Alternative: Contrastive Explanations for Speech-to-Text Modelscited by 3
  28. 2023Good, but not always Fair: An Evaluation of Gender Bias for three commercial Machine Translation Systemscited by 2
  29. 2023How Transferable are Attribute Controllers on Pretrained Multilingual Translation Models?cited by 2
  30. 2023How To Build Competitive Multi-gender Speech Translation Models For Controlling Speaker Gender Translationcited by 2
  31. 2024Data Bias According to Bipol: Men are Naturally Right and It is the Role of Women to Follow Their Leadcited by 2
  32. 2025What Triggers my Model? Contrastive Explanations Inform Gender Choices by Translation Modelscited by 2
  33. 2023Efficient CTC Regularization via Coarse Labels for End-to-End Speech Translationcited by 1
  34. 2026Gender Disambiguation in Machine Translation: Diagnostic Evaluation in Decoder-Only Architecturescited by 1
  35. 2026Why We Need Speech to Evaluate Speech Translationcited by 1
  36. 2026Voice or Stereotype? Disentangling Acoustic and Content-Based Gender in Speech-to-Speech Modelscited by 1
  37. 2023Don't Overlook the Grammatical Gender: Bias Evaluation for Hindi-English Machine Translationcited by 0
  38. 2024Fine-grained Gender Control in Machine Translation with Large Language Modelscited by 0

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