mlsanityData to 7 Oct 2026

Unveiling Biases while Embracing Sustainability: Assessing the Dual Challenges of Automatic Speech Recognition Systems

arXiv 2503.00907 ↗First posted 2 Mar 2025cs.CL cs.SD eess.AScited by 4

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Abstract (arXiv)

In this paper, we present a bias and sustainability focused investigation of Automatic Speech Recognition (ASR) systems, namely Whisper and Massively Multilingual Speech (MMS), which have achieved state-of-the-art (SOTA) performances. Despite their improved performance in controlled settings, there remains a critical gap in understanding their efficacy and equity in real-world scenarios. We analyze ASR biases w.r.t. gender, accent, and age group, as well as their effect on downstream tasks. In addition, we examine the environmental impact of ASR systems, scrutinizing the use of large acoustic models on carbon emission and energy consumption. We also provide insights into our empirical analyses, offering a valuable contribution to the claims surrounding bias and sustainability in ASR systems.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 17 of its 33 references that are in the map. First posted on arXiv 2 Mar 2025.

Builds on

17 of 33 references.

References · 17

  1. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
  2. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  3. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,242
  4. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
  5. 2023Scaling Speech Technology to 1,000+ Languagescited by 401
  6. 2020Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learningcited by 246
  7. 2023Google USM: Scaling Automatic Speech Recognition Beyond 100 Languagescited by 228
  8. 2019Fairness in Deep Learning: A Computational Perspectivecited by 123
  9. 2022Measuring the Carbon Intensity of AI in Cloud Instancescited by 110
  10. 2021Towards Measuring Fairness in AI: the Casual Conversations Datasetcited by 80
  11. 2022Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AIcited by 58
  12. 2023The Edinburgh International Accents of English Corpus: Towards the Democratization of English ASRcited by 36
5 more
  1. 2022ASR2K: Speech Recognition for Around 2000 Languages without Audiocited by 26
  2. 2019Gender Representation in French Broadcast Corpora and Its Impact on ASR Performancecited by 23
  3. 2024The Balancing Act: Unmasking and Alleviating ASR Biases in Portuguesecited by 6
  4. 2023Can Voice Assistants Be Microaggressors? Cross-Race Psychological Responses to Failures of Automatic Speech Recognitioncited by 5
  5. 2023Using Data Augmentations and VTLN to Reduce Bias in Dutch End-to-End Speech Recognition Systemscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Benchmarking Akan ASR Models Across Domain-Specific Datasets: A Comparative Evaluation of Performance, Scalability, and Adaptabilitycited by 2
  2. 2026VIBE: Voice-Induced open-ended Bias Evaluation for Large Audio-Language Models via Real-World Speechcited by 2
  3. 2026Demographic and Linguistic Bias Evaluation in Omnimodal Language Modelscited by 0
  4. 2026Assessing the Energy and Carbon Emissions of Neural Speaker Verification Model in Training and Inferencecited by 0