# Evaluating Gender Bias in Speech Translation

arXiv: https://arxiv.org/abs/2010.14465 · HTML: https://mlsanity.com/paper/2010.14465/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2020.

## Built on by (most cited first, 19 of 19)

- [Gender Bias in Machine Translation](https://mlsanity.com/paper/2104.06001/) (2021)
- [Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation](https://mlsanity.com/paper/2203.09866/) (2022)
- [Listen and Speak Fairly: A Study on Semantic Gender Bias in Speech Integrated Large Language Models](https://mlsanity.com/paper/2407.06957/) (2024)
- [ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy Models](https://mlsanity.com/paper/2406.09334/) (2024)
- [Hey ASR System! Why Aren't You More Inclusive? Automatic Speech Recognition Systems' Bias and Proposed Bias Mitigation Techniques. A Literature Review](https://mlsanity.com/paper/2211.09511/) (2022)
- [Spoken Stereoset: On Evaluating Social Bias Toward Speaker in Speech Large Language Models](https://mlsanity.com/paper/2408.07665/) (2024)
- [A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems](https://mlsanity.com/paper/2204.01397/) (2022)
- [What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-centered Study](https://mlsanity.com/paper/2410.00545/) (2024)
- [How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation](https://mlsanity.com/paper/2105.13782/) (2021)
- [FLEURS-ASL: Including American Sign Language in Massively Multilingual Multitask Evaluation](https://mlsanity.com/paper/2408.13585/) (2024)
- [Hearing to Translate: The Effectiveness of Speech Modality Integration into LLMs](https://mlsanity.com/paper/2512.16378/) (2025)
- [VoiceBBQ: Investigating Effect of Content and Acoustics in Social Bias of Spoken Language Model](https://mlsanity.com/paper/2509.21108/) (2025)
- [The Makerere Radio Speech Corpus: A Luganda Radio Corpus for Automatic Speech Recognition](https://mlsanity.com/paper/2206.09790/) (2022)
- [Voice, Bias, and Coreference: An Interpretability Study of Gender in Speech Translation](https://mlsanity.com/paper/2511.21517/) (2025)
- [On the Encoding of Gender in Transformer-based ASR Representations](https://mlsanity.com/paper/2406.09855/) (2024)
- [Efficient Compression of Multitask Multilingual Speech Models](https://mlsanity.com/paper/2405.00966/) (2024)
- [On the Role of Speech Data in Reducing Toxicity Detection Bias](https://mlsanity.com/paper/2411.08135/) (2024)
- [Voice or Stereotype? Disentangling Acoustic and Content-Based Gender in Speech-to-Speech Models](https://mlsanity.com/paper/2609.09263/) (2026)
- [Personalization Without Stereotyping: Do Speech LLMs Know When to Turn a Deaf Ear?](https://mlsanity.com/paper/2610.13550/) (2026)

## Abstract (arXiv)

The scientific community is increasingly aware of the necessity to embrace pluralism and consistently represent major and minor social groups. Currently, there are no standard evaluation techniques for different types of biases. Accordingly, there is an urgent need to provide evaluation sets and protocols to measure existing biases in our automatic systems. Evaluating the biases should be an essential step towards mitigating them in the systems. This paper introduces WinoST, a new freely available challenge set for evaluating gender bias in speech translation. WinoST is the speech version of WinoMT which is a MT challenge set and both follow an evaluation protocol to measure gender accuracy. Using a state-of-the-art end-to-end speech translation system, we report the gender bias evaluation on four language pairs and we show that gender accuracy in speech translation is more than 23% lower than in MT.