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English(EN) Addressing Pitfalls in Auditing Practices of Automatic Speech Recognition Technologies: A Case Study of People with Aphasia

研究发现：ASR审计缺陷掩盖了对失语症患者的伤害

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新发表在arXiv上的研究论文指出了当前自动语音识别（ASR）系统审计实践中的关键缺陷。研究强调，标准方法可能掩盖性能差异，特别是对于患有失语症等言语障碍的个体。作者提出了一个更全面的审计框架，以解决文本标准化不足、子群体分析不充分以及过度依赖单一指标等问题，并倡导以社区驱动的方法来确保ASR性能的公平性。 AI

影响 强调了对更公平的AI审计的需求，特别是针对有言语障碍的边缘用户群体。

排序理由 该集群包含一篇详细介绍研究发现并提出新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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研究发现：ASR审计缺陷掩盖了对失语症患者的伤害

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该集群包含一篇详细介绍研究发现并提出新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Katelyn Xiaoying Mei, Anna Seo Gyeong Choi, Hilke Schellmann, Mona Sloane, Allison Koenecke ·

    Addressing Pitfalls in Auditing Practices of Automatic Speech Recognition Technologies: A Case Study of People with Aphasia

    arXiv:2506.08846v3 Announce Type: replace-cross Abstract: Automatic Speech Recognition (ASR) systems' growing use warrants robust auditing approaches to ensure equitable transcription quality, especially for people with speech disorders like aphasia who disproportionately depend …

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