mlsanityData to 7 Oct 2026

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

arXiv 1908.08717 ↗First posted 23 Aug 2019cs.CL cs.SD eess.AScited by 23

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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.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Aug 2019.

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Citing papers per year: 2021: 2; 2022: 3; 2023: 3; 2024: 8; 2025: 3; 2026: 4.

23 papers.

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  1. 2021Quantifying Bias in Automatic Speech Recognitioncited by 50
  2. 2022Toward Fairness in Speech Recognition: Discovery and mitigation of performance disparitiescited by 27
  3. 2024Twists, Humps, and Pebbles: Multilingual Speech Recognition Models Exhibit Gender Performance Gapscited by 24
  4. 2024Listen and Speak Fairly: A Study on Semantic Gender Bias in Speech Integrated Large Language Modelscited by 20
  5. 2022Hey ASR System! Why Aren't You More Inclusive? Automatic Speech Recognition Systems' Bias and Proposed Bias Mitigation Techniques. A Literature Reviewcited by 19
  6. 2022A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systemscited by 14
  7. 2021How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translationcited by 11
  8. 2024The Balancing Act: Unmasking and Alleviating ASR Biases in Portuguesecited by 6
  9. 2024Sonos Voice Control Bias Assessment Dataset: A Methodology for Demographic Bias Assessment in Voice Assistantscited by 6
  10. 2023A Deep Dive into the Disparity of Word Error Rates Across Thousands of NPTEL MOOC Videoscited by 5
  11. 2025Voice, Bias, and Coreference: An Interpretability Study of Gender in Speech Translationcited by 5
  12. 2023No Pitch Left Behind: Addressing Gender Unbalance in Automatic Speech Recognition through Pitch Manipulationcited by 4
11 more
  1. 2024On the Encoding of Gender in Transformer-based ASR Representationscited by 4
  2. 2025Unveiling Biases while Embracing Sustainability: Assessing the Dual Challenges of Automatic Speech Recognition Systemscited by 4
  3. 2024DENOASR: Debiasing ASRs through Selective Denoisingcited by 3
  4. 2025How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measurescited by 3
  5. 2023Using Data Augmentations and VTLN to Reduce Bias in Dutch End-to-End Speech Recognition Systemscited by 2
  6. 2024On the Role of Speech Data in Reducing Toxicity Detection Biascited by 2
  7. 2026Greater accessibility can amplify discrimination in generative AIcited by 1
  8. 2026Where Do Self-Supervised Speech Models Become Unfair?cited by 1
  9. 2024Everyone deserves their voice to be heard: Analyzing Predictive Gender Bias in ASR Models Applied to Dutch Speech Datacited by 0
  10. 2026Dialect and Gender Bias in YouTube's Spanish Captioning Systemcited by 0
  11. 2026Towards participatory speech dataset curation: A queer case study and conceptual frameworkcited by 0