Modern automated accessibility testing tools for mobile applications have significantly improved the detection of interface violations, yet their impact on remediation remains limited. A key reason is that existing tools typically produce low-level, technical outputs that are difficult for non-specialist stakeholders, such as product managers and designers, to interpret in terms of real user harm and compliance risk. In this paper, we present \textsc{HEAR} (\underline{H}uman-c\underline{E}ntered \underline{A}ccessibility \underline{R}eporting), a framework that bridges this interpretation gap by transforming raw accessibility bug reports into empathetic, stakeholder-oriented narratives. Given the outputs of the existing accessibility testing tool, \textsc{HEAR} first reconstructs the UI context through semantic slicing and visual grounding, then dynamically injects disability-oriented personas matched to each violation type, and finally performs multi-layer reasoning to explain the physical barrier, functional blockage, and relevant legal or compliance concerns. We evaluate the framework on real-world accessibility issues collected from four popular Android applications and conduct a user study (N=12). The results show that \textsc{HEAR} generates factually grounded reports and substantially improves perceived empathy, urgency, persuasiveness, and awareness of legal risk compared with raw technical logs, while imposing little additional cognitive burden.

翻译：现代移动应用的自动化可访问性测试工具虽显著提升了界面违规检测能力，但其对修复工作的促进作用仍十分有限。核心原因在于现有工具通常输出低层级技术性报告，导致产品经理、设计师等非专业利益相关者难以从实际用户伤害与合规风险角度进行解读。本文提出HEAR（以人为中心的可访问性报告）框架，通过将原始可访问性缺陷报告转化为具有共情意识、面向利益相关者的叙事文本，弥合了这一解释鸿沟。HEAR首先基于现有可访问性测试工具的输出结果，通过语义切分与视觉锚定重建用户界面上下文，随后针对每种违规类型动态注入匹配的残障用户画像，最终执行多层次推理以阐释物理障碍、功能阻断及相关的法律合规风险。我们在四款主流安卓应用中采集真实可访问性问题进行框架评估，并开展用户研究（N=12）。结果表明：相较于原始技术日志，HEAR生成的事实性报告显著提升了感知共情度、紧迫性、说服力及法律风险认知度，同时几乎不增加认知负荷。