一个基于知识的框架,用于使用语音识别和扬声器验证整合手术安全检查.
Frontiers in neuroscience
|January 28, 2026
概括
一个新的基于知识的框架,集成自动语音识别和扬声器验证,提高了在杂的手术室手术安全清单的准确性. 该系统通过考虑语音内容,发言人身份和检查清单规则来提高可靠性.
科学领域:
- 医疗信息学 医疗信息学
- 语音识别 语言识别
- 人工智能的人工智能
背景情况:
- 手术室面临着重要的音频挑战,包括重叠的语音和高噪音水平.
- 现有的只有自动语音识别 (ASR) 的系统由于这些因素和缺乏说话者身份而难以准确.
- 世卫组织的外科安全检查清单旨在减少错误,但在现实世界OR环境中容易出现误识.
研究的目的:
- 开发和评估一个综合框架,将ASR和扬声器验证 (SV) 结合为外科安全提供知识驱动的规则引擎.
- 为了提高复杂的手术室音频条件下检查清单项目验证的准确性和可靠性.
- 提供实时系统提示,指示检查列表状态 (通过,故障,报警,不确定).
主要方法:
- 设计了一个集成的ASR+SV框架,结合了基于世界卫生组织检查清单的知识驱动规则引擎.
- 多通道音频使用基于Conformer的ASR和ECAPA-TDNN SV模型进行处理.
- 一个规则层使用本体学和冲突解决规则强制执行一致性,比较ASR-only,ASR+SV和全知识驱动方法.
主要成果:
- 完整的框架将Word错误率 (WER) 从18.7%降至13.5%,并在杂的多音箱环境下实现了3.1%的等错率 (EER).
- 检查清单验证准确率达到93.8%,报警率约为2.7%.
- 该系统在高达1.5米的说话距离和60dB的背景噪声下表现出强大的性能,纠正语义和基于角色的错误.
结论:
- 基于知识的ASR+SV框架为智能手术室提供了一个可解释和可审计的解决方案,提高语义正确性和扬声器验证.
- 它为增强的"停机"和"操作审查"功能提供了基础.
- 未来的工作包括临床试点研究和与多式 OR 数据的整合,解决隐私和工作流接受.
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