mlsanityData to 14 Oct 2026

Model-Based Approach for Measuring the Fairness in ASR

arXiv 2109.09061 ↗First posted 19 Sep 2021stat.ML cs.CY cs.LGcited by 13

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Abstract (arXiv)

The issue of fairness arises when the automatic speech recognition (ASR) systems do not perform equally well for all subgroups of the population. In any fairness measurement studies for ASR, the open questions of how to control the nuisance factors, how to handle unobserved heterogeneity across speakers, and how to trace the source of any word error rate (WER) gap among different subgroups are especially important - if not appropriately accounted for, incorrect conclusions will be drawn. In this paper, we introduce mixed-effects Poisson regression to better measure and interpret any WER difference among subgroups of interest. Particularly, the presented method can effectively address the three problems raised above and is very flexible to use in practical disparity analyses. We demonstrate the validity of proposed model-based approach on both synthetic and real-world speech data.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 7 of its 29 references that are in the map. First posted on arXiv 19 Sep 2021.

Builds on

7 of 29 references.

References · 7

  1. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  3. 2016Enriching Word Vectors with Subword Informationcited by 1,583
  4. 2016Semantics derived automatically from language corpora contain human-like biasescited by 901
  5. 2016Demographic Dialectal Variation in Social Media: A Case Study of African-American Englishcited by 196
  6. 2020Emformer: Efficient Memory Transformer Based Acoustic Model For Low Latency Streaming Speech Recognitioncited by 113
  7. 2020Investigating Bias and Fairness in Facial Expression Recognitioncited by 87

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 4; 2023: 1; 2024: 4; 2025: 2; 2026: 2.

13 papers.

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  1. 2024Towards measuring fairness in speech recognition: Fair-Speech datasetcited by 26
  2. 2022Hey ASR System! Why Aren't You More Inclusive? Automatic Speech Recognition Systems' Bias and Proposed Bias Mitigation Techniques. A Literature Reviewcited by 19
  3. 2022A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systemscited by 15
  4. 2022A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairnesscited by 13
  5. 2023Pre-trained Speech Processing Models Contain Human-Like Biases that Propagate to Speech Emotion Recognitioncited by 9
  6. 2024A Contrastive Learning Approach to Mitigate Bias in Speech Modelscited by 9
  7. 2022Global Performance Disparities Between English-Language Accents in Automatic Speech Recognitioncited by 6
  8. 2024Sonos Voice Control Bias Assessment Dataset: A Methodology for Demographic Bias Assessment in Voice Assistantscited by 6
  9. 2025How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measurescited by 3
  10. 2024Everyone deserves their voice to be heard: Analyzing Predictive Gender Bias in ASR Models Applied to Dutch Speech Datacited by 0
  11. 2025Towards Fair ASR For Second Language Speakers Using Fairness Prompted Finetuningcited by 0
  12. 2026Measuring Fairness in Large Audio Language Models via Semantic-Aware Bias Estimationcited by 0
1 more
  1. 2026A Cross Community Agenda for Speech AIcited by 0