mlsanityData to 7 Oct 2026

A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairness

arXiv 2212.09006 ↗First posted 18 Dec 2022cs.SD cs.LG eess.AScited by 13

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Abstract (arXiv)

Speech-centric machine learning systems have revolutionized many leading domains ranging from transportation and healthcare to education and defense, profoundly changing how people live, work, and interact with each other. However, recent studies have demonstrated that many speech-centric ML systems may need to be considered more trustworthy for broader deployment. Specifically, concerns over privacy breaches, discriminating performance, and vulnerability to adversarial attacks have all been discovered in ML research fields. In order to address the above challenges and risks, a significant number of efforts have been made to ensure these ML systems are trustworthy, especially private, safe, and fair. In this paper, we conduct the first comprehensive survey on speech-centric trustworthy ML topics related to privacy, safety, and fairness. In addition to serving as a summary report for the research community, we point out several promising future research directions to inspire the researchers who wish to explore further in this area.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 127 of its 212 references that are in the map. First posted on arXiv 18 Dec 2022.

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127 of 212 references.

References · 127

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Citing papers per year: 2023: 3; 2024: 6; 2025: 4.

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  1. 2023FedMultimodal: A Benchmark For Multimodal Federated Learningcited by 51
  2. 2023On the Intersection of Self-Correction and Trust in Language Modelscited by 12
  3. 2024Innovative Speech-Based Deep Learning Approaches for Parkinson's Disease Classification: A Systematic Reviewcited by 7
  4. 2025Trustworthy Machine Learning via Memorization and the Granular Long-Tail: A Survey on Interactions, Tradeoffs, and Beyondcited by 6
  5. 2023Understanding Spoken Language Development of Children with ASD Using Pre-trained Speech Embeddingscited by 3
  6. 2024Promoting the Responsible Development of Speech Datasets for Mental Health and Neurological Disorders Researchcited by 3
  7. 2025Gender Fairness of Machine Learning Algorithms for Pain Detectioncited by 3
  8. 2024Improving Membership Inference in ASR Model Auditing with Perturbed Loss Featurescited by 2
  9. 2025WavShape: Information-Theoretic Speech Representation Learning for Fair and Privacy-Aware Audio Processingcited by 2
  10. 2024Speech After Gender: A Trans-Feminine Perspective on Next Steps for Speech Science and Technologycited by 1
  11. 2025Assessing the Effectiveness of Membership Inference on Generative Musiccited by 1
  12. 2024TI-ASU: Toward Robust Automatic Speech Understanding through Text-to-speech Imputation Against Missing Speech Modalitycited by 0
1 more
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