Toward Fair Speech Technologies: A Comprehensive Survey of Bias and Fairness in Speech AI

2026-05-02

AI Summary

This work addresses the pressing challenge of fairness in speech AI within high-stakes scenarios, where existing research remains fragmented across tasks and disciplines, lacking a unified framework that accounts for speech-specific bias mechanisms. The paper proposes the first systematic fairness framework encompassing speech generation, perception, and spoken language models. It formally defines seven speech-adapted fairness criteria, introduces an evolutionary perspective through three paradigms—robustness, representation, and governance—and uniquely incorporates speech-specific factors such as channel-induced bias and subjectivity in emotional annotation into bias diagnostics. Drawing on a synthesis of over 400 studies, the authors develop mathematically grounded evaluation metrics, a decision-tree-based model selection methodology, and a four-stage intervention strategy, yielding an end-to-end guideline for bias diagnosis and mitigation across the speech processing pipeline, while delineating open challenges and future research directions.

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.

Problem

• speech AI
• bias
• fairness
• speech technologies
• demographic bias

Innovation

• speech fairness
• bias diagnosis
• fairness framework
• speech-specific bias
• mitigation strategies