A Study on Bias and Fairness In Deep Speaker Recognition

• URL: http://arxiv.org/abs/2303.08026v1
• Date: Tue, 14 Mar 2023 16:08:19 GMT
• Title: A Study on Bias and Fairness In Deep Speaker Recognition
• Abstract summary: We study the notion of fairness in recent speaker recognition (SR) systems based on 3 popular definitions, namely Statistical Parity, Equalized Odds, and Equal Opportunity. We examine 5 popular neural architectures and 5 commonly used loss functions in training SR systems, while evaluating their fairness against gender and nationality groups.
• Score: 25.71206255965502
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the ubiquity of smart devices that use speaker recognition (SR) systems as a means of authenticating individuals and personalizing their services, fairness of SR systems has becomes an important point of focus. In this paper we study the notion of fairness in recent SR systems based on 3 popular and relevant definitions, namely Statistical Parity, Equalized Odds, and Equal Opportunity. We examine 5 popular neural architectures and 5 commonly used loss functions in training SR systems, while evaluating their fairness against gender and nationality groups. Our detailed experiments shed light on this concept and demonstrate that more sophisticated encoder architectures better align with the definitions of fairness. Additionally, we find that the choice of loss functions can significantly impact the bias of SR models.

Related papers

• Responsible Benchmarking of Fairness for Automatic Speech Recognition [44.6396086462419]
  Many studies have shown automatic speech processing (ASR) systems have unequal performance across speakergroups (SG's)<n>We lay out best practices for benchmarking ASR fairness based on literaturefrom machine learning fairness, social sciences, and speech science.<n>We advocate for as fine-grained an analysis as possibleof the intersectionality of as many demographic variables as are available in the metadata of fairness corpora.
  arXiv  Detail & Related papers  (2026-05-11T14:11:50Z)
• Thinking Before Matching: A Reinforcement Reasoning Paradigm Towards General Person Re-Identification [77.51622646061314]
  ReID-R is proposed as a novel reasoning-driven paradigm that achieves explicit identity understanding and reasoning.<n>Experiments on multiple ReID benchmarks demonstrate that ReID-R achieves competitive identity discrimination as superior methods.
  arXiv  Detail & Related papers  (2026-04-21T08:24:07Z)
• SAVOIR: Learning Social Savoir-Faire via Shapley-based Reward Attribution [82.31558282651811]
  Social intelligence, the ability to navigate complex interpersonal interactions, presents a fundamental challenge for language agents.<n>Existing approaches directly employ language models to distribute episode-level rewards, yielding attributions that are retrospective and lack theoretical grounding.<n>We propose SAVOIR, a novel principled framework grounded in cooperative game theory.
  arXiv  Detail & Related papers  (2026-04-21T02:08:25Z)
• Interpretable Debiasing of Vision-Language Models for Social Fairness [55.85977929985967]
  We introduce an interpretable, model-agnostic bias mitigation framework, DeBiasLens, that localizes social attribute neurons in Vision-Language models.<n>We train SAEs on facial image or caption datasets without corresponding social attribute labels to uncover neurons highly responsive to specific demographics.<n>Our research lays the groundwork for future auditing tools, prioritizing social fairness in emerging real-world AI systems.
  arXiv  Detail & Related papers  (2026-02-27T13:37:11Z)
• Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts [79.18608192761512]
  Self-Explainable Models (SEMs) rely on Prototypical Concept Learning (PCL) to enable their visual recognition processes more interpretable.<n>We propose a Few-Shot Prototypical Concept Classification framework that mitigates two key challenges under low-data regimes: parametric imbalance and representation misalignment.<n>Our approach consistently outperforms existing SEMs by a notable margin, with 4.2%-8.7% relative gains in 5-way 5-shot classification.
  arXiv  Detail & Related papers  (2025-06-05T06:39:43Z)
• ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems [3.8947802481286478]
  We introduce the ASR-FAIRBENCH leaderboard which is designed to assess both the accuracy and equity of ASR models in real-time.<n>Our approach reveals significant performance disparities in SOTA ASR models across demographic groups and offers a benchmark to drive the development of more inclusive ASR technologies.
  arXiv  Detail & Related papers  (2025-05-16T11:31:31Z)
• Mitigating Bias in Facial Recognition Systems: Centroid Fairness Loss Optimization [9.537960917804993]
  societal demand for fair AI systems has put pressure on the research community to develop predictive models that meet new fairness criteria. In particular, the variability of the errors made by certain Facial Recognition (FR) systems across specific segments of the population compromises the deployment of the latter. We propose a novel post-processing approach to improve the fairness of pre-trained FR models by optimizing a regression loss which acts on centroid-based scores.
  arXiv  Detail & Related papers  (2025-04-27T22:17:44Z)
• Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented Generation [53.285436927963865]
  This paper presents the first systematic evaluation of RAG systems integrated with fair rankings. We focus specifically on measuring the fair exposure of each relevant item across the rankings utilized by RAG systems. Our findings indicate that RAG systems with fair rankings can maintain a high level of generation quality and, in many cases, even outperform traditional RAG systems.
  arXiv  Detail & Related papers  (2024-09-17T23:10:04Z)
• FairLENS: Assessing Fairness in Law Enforcement Speech Recognition [37.75768315119143]
  We propose a novel and adaptable evaluation method to examine the fairness disparity between different models. We conducted fairness assessments on 1 open-source and 11 commercially available state-of-the-art ASR models.
  arXiv  Detail & Related papers  (2024-05-21T19:23:40Z)
• Toward Practical Automatic Speech Recognition and Post-Processing: a Call for Explainable Error Benchmark Guideline [12.197453599489963]
  We propose the development of an Error Explainable Benchmark (EEB) dataset. This dataset, while considering both speech- and text-level, enables a granular understanding of the model's shortcomings. Our proposition provides a structured pathway for a more real-world-centric' evaluation, allowing for the detection and rectification of nuanced system weaknesses.
  arXiv  Detail & Related papers  (2024-01-26T03:42:45Z)
• DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision [73.80009454050858]
  This work presents a self-supervised model, called DualFair, that can debias sensitive attributes like gender and race from learned representations. Our model jointly optimize for two fairness criteria - group fairness and counterfactual fairness.
  arXiv  Detail & Related papers  (2023-03-15T07:13:54Z)
• Fairness in Matching under Uncertainty [78.39459690570531]
  algorithmic two-sided marketplaces have drawn attention to the issue of fairness in such settings. We axiomatize a notion of individual fairness in the two-sided marketplace setting which respects the uncertainty in the merits. We design a linear programming framework to find fair utility-maximizing distributions over allocations.
  arXiv  Detail & Related papers  (2023-02-08T00:30:32Z)
• Textural-Structural Joint Learning for No-Reference Super-Resolution Image Quality Assessment [59.91741119995321]
  We develop a dual stream network to jointly explore the textural and structural information for quality prediction, dubbed TSNet. By mimicking the human vision system (HVS) that pays more attention to the significant areas of the image, we develop the spatial attention mechanism to make the visual-sensitive areas more distinguishable. Experimental results show the proposed TSNet predicts the visual quality more accurate than the state-of-the-art IQA methods, and demonstrates better consistency with the human's perspective.
  arXiv  Detail & Related papers  (2022-05-27T09:20:06Z)
• Fairness Indicators for Systematic Assessments of Visual Feature Extractors [21.141633753573764]
  We propose three fairness indicators, which aim at quantifying harms and biases of visual systems. Our indicators use existing publicly available datasets collected for fairness evaluations. These indicators are not intended to be a substitute for a thorough analysis of the broader impact of the new computer vision technologies.
  arXiv  Detail & Related papers  (2022-02-15T17:45:33Z)
• Statistical discrimination in learning agents [64.78141757063142]
  Statistical discrimination emerges in agent policies as a function of both the bias in the training population and of agent architecture. We show that less discrimination emerges with agents that use recurrent neural networks, and when their training environment has less bias.
  arXiv  Detail & Related papers  (2021-10-21T18:28:57Z)
• Discovering "Semantics" in Super-Resolution Networks [54.45509260681529]
  Super-resolution (SR) is a fundamental and representative task of low-level vision area. It is generally thought that the features extracted from the SR network have no specific semantic information. Can we find any "semantics" in SR networks?
  arXiv  Detail & Related papers  (2021-08-01T09:12:44Z)
• Improving Fairness in Speaker Recognition [4.94706680113206]
  We investigate the disparity in performance achieved by state-of-the-art deep speaker recognition systems. We show that models trained with demographically-balanced training sets exhibit a fairer behavior on different groups, while still being accurate.
  arXiv  Detail & Related papers  (2021-04-29T01:08:53Z)
• Domain-Incremental Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognition [5.478764356647437]
  We propose the novel usage of Continual Learning (CL) as a potent bias mitigation method to enhance the fairness of FER systems. We compare different non-CL-based and CL-based methods for their classification accuracy and fairness scores on expression recognition and Action Unit (AU) detection tasks. Our experimental results show that CL-based methods, on average, outperform other popular bias mitigation techniques on both accuracy and fairness metrics.
  arXiv  Detail & Related papers  (2021-03-15T18:22:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.