# Gender Representation in French Broadcast Corpora and Its Impact on ASR Performance

arXiv: https://arxiv.org/abs/1908.08717 · HTML: https://mlsanity.com/paper/1908.08717/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2019.

## Built on by (most cited first, 23 of 23)

- [Quantifying Bias in Automatic Speech Recognition](https://mlsanity.com/paper/2103.15122/) (2021)
- [Toward Fairness in Speech Recognition: Discovery and mitigation of performance disparities](https://mlsanity.com/paper/2207.11345/) (2022)
- [Twists, Humps, and Pebbles: Multilingual Speech Recognition Models Exhibit Gender Performance Gaps](https://mlsanity.com/paper/2402.17954/) (2024)
- [Listen and Speak Fairly: A Study on Semantic Gender Bias in Speech Integrated Large Language Models](https://mlsanity.com/paper/2407.06957/) (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)
- [A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems](https://mlsanity.com/paper/2204.01397/) (2022)
- [How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation](https://mlsanity.com/paper/2105.13782/) (2021)
- [The Balancing Act: Unmasking and Alleviating ASR Biases in Portuguese](https://mlsanity.com/paper/2402.07513/) (2024)
- [Sonos Voice Control Bias Assessment Dataset: A Methodology for Demographic Bias Assessment in Voice Assistants](https://mlsanity.com/paper/2405.19342/) (2024)
- [A Deep Dive into the Disparity of Word Error Rates Across Thousands of NPTEL MOOC Videos](https://mlsanity.com/paper/2307.10587/) (2023)
- [Voice, Bias, and Coreference: An Interpretability Study of Gender in Speech Translation](https://mlsanity.com/paper/2511.21517/) (2025)
- [No Pitch Left Behind: Addressing Gender Unbalance in Automatic Speech Recognition through Pitch Manipulation](https://mlsanity.com/paper/2310.06590/) (2023)
- [On the Encoding of Gender in Transformer-based ASR Representations](https://mlsanity.com/paper/2406.09855/) (2024)
- [Unveiling Biases while Embracing Sustainability: Assessing the Dual Challenges of Automatic Speech Recognition Systems](https://mlsanity.com/paper/2503.00907/) (2025)
- [DENOASR: Debiasing ASRs through Selective Denoising](https://mlsanity.com/paper/2410.16712/) (2024)
- [How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures](https://mlsanity.com/paper/2507.05885/) (2025)
- [Using Data Augmentations and VTLN to Reduce Bias in Dutch End-to-End Speech Recognition Systems](https://mlsanity.com/paper/2307.02009/) (2023)
- [On the Role of Speech Data in Reducing Toxicity Detection Bias](https://mlsanity.com/paper/2411.08135/) (2024)
- [Greater accessibility can amplify discrimination in generative AI](https://mlsanity.com/paper/2603.22260/) (2026)
- [Where Do Self-Supervised Speech Models Become Unfair?](https://mlsanity.com/paper/2604.18249/) (2026)
- [Everyone deserves their voice to be heard: Analyzing Predictive Gender Bias in ASR Models Applied to Dutch Speech Data](https://mlsanity.com/paper/2411.09431/) (2024)
- [Dialect and Gender Bias in YouTube's Spanish Captioning System](https://mlsanity.com/paper/2602.24002/) (2026)
- [Towards participatory speech dataset curation: A queer case study and conceptual framework](https://mlsanity.com/paper/2609.25496/) (2026)

## Abstract (arXiv)

This paper analyzes the gender representation in four major corpora of French broadcast. These corpora being widely used within the speech processing community, they are a primary material for training automatic speech recognition (ASR) systems. As gender bias has been highlighted in numerous natural language processing (NLP) applications, we study the impact of the gender imbalance in TV and radio broadcast on the performance of an ASR system. This analysis shows that women are under-represented in our data in terms of speakers and speech turns. We introduce the notion of speaker role to refine our analysis and find that women are even fewer within the Anchor category corresponding to prominent speakers. The disparity of available data for both gender causes performance to decrease on women. However this global trend can be counterbalanced for speaker who are used to speak in the media when sufficient amount of data is available.