Toward Fair Speech Technologies: A Comprehensive Survey of Bias and Fairness in Speech AI
Abstract
Speech technologies are deployed in high-stakes settings, yet fairness concerns remain fragmented across tasks and disciplines. Existing surveys either adopt a general machine-learning perspective that overlooks speech-specific properties or focus on a single task, missing failure patterns shared across the speech domain. Synthesizing over 400 studies spanning generation and perception tasks and emerging speech-language models, this survey presents a unified framework that links formal fairness definitions to evaluation, diagnosis, and mitigation. We formalize seven fairness definitions adapted to the speech modality and organize the field's conceptual evolution through three paradigms: Robustness, Representation, and Governance. We then ground evaluation metrics in the mathematical cores of these definitions and offer a decision tree for metric selection. We diagnose bias sources along the speech processing pipeline, surfacing speech-specific mechanisms such as channel bias as a demographic proxy and annotation subjectivity in emotion labels. We systematize mitigation strategies across four intervention stages, mapping each to the diagnosed sources. Finally, we identify open challenges and propose directions for future research.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper serves as a foundational survey that systematically addresses bias and fairness in speech AI, providing a comprehensive framework that can guide future research and development in this critical area. The authors' approach to synthesizing existing literature and formalizing fairness definitions is a significant contribution to the field, setting the stage for more equitable speech technologies.
Methodology
The paper presents a comprehensive survey that synthesizes over 400 studies related to bias and fairness in speech AI, establishing a unified framework that links formal fairness definitions to evaluation metrics, bias diagnosis, and mitigation strategies. The authors formalize seven fairness definitions specifically adapted to the speech modality and provide a decision tree for metric selection, which is a novel contribution to the field. The methodology is robust, drawing on a wide range of literature and systematically addressing the unique challenges posed by the speech domain.
Results & evaluation
While the paper is primarily a survey and does not include original experimental results, it effectively reviews existing literature and identifies gaps in current methodologies. It categorizes bias sources along the speech processing pipeline and systematizes mitigation strategies, which could serve as a foundation for future empirical studies. The depth of analysis into bias mechanisms and fairness paradigms is commendable, although the lack of original experimental validation limits the immediate applicability of the findings.
Reproducibility
The survey does not present original experiments, thus reproducibility in the traditional sense does not apply. However, the clear organization of existing literature and the proposed frameworks allow for future researchers to build upon this work in a reproducible manner. The decision tree for metric selection is particularly useful for guiding future empirical studies.
Limitations
One limitation of the paper is its reliance on existing literature without presenting new empirical data or case studies to validate the proposed frameworks. Additionally, while the survey covers a wide range of topics, it may not address all nuances of bias and fairness in speech technologies, particularly in emerging areas of research. The authors also acknowledge the complexity of navigating fairness in sociotechnical contexts, which may not be fully captured in their framework.
Broader impact
The implications of this work are significant, as it addresses critical issues of bias and fairness in speech technologies that are increasingly deployed in high-stakes environments. By highlighting the need for fairness as a core requirement rather than an afterthought, the paper encourages researchers and practitioners to consider the ethical implications of their technologies. This survey could influence future research directions and policy-making in the field of AI and speech technology. This paper serves as a foundational survey that systematically addresses bias and fairness in speech AI, providing a comprehensive framework that can guide future research and development in this critical area. The authors' approach to synthesizing existing literature and formalizing fairness definitions is a significant contribution to the field, setting the stage for more equitable speech technologies.
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