pDenoiser:一个个性化的语音增强神经网络,用于医院前紧急医疗服务
概括
本研究介绍了pDenoiser,这是一个针对紧急医疗服务 (EMS) 的个性化语音增强工具. 它在杂的环境中提高了医生的声音清晰度,提高了语音识别准确度,以改善沟通.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 医院前紧急医疗服务 (EMS) 通信面临重大挑战,原因是复杂的噪音干扰,特别是来自其他语音.
- 这种噪音阻碍了自动语音识别 (ASR) 技术的有效性和整体电话通信效率.
研究的目的:
- 开发一种新的技术,在EMS操作期间从噪音和混合音频中提取目标医生的语音.
- 在具有挑战性的医院前环境中提高语音质量和ASR准确性.
主要方法:
- 提出pDenoiser,一个实时,单声个性化语音增强 (PSE) 神经网络,运行在时间域.
- 利用紧急医生的先前发声线索,选择性地增强目标语音,同时抑制噪音和非目标语音.
主要成果:
- 对公共和自主收集的EMS数据集的评估显示出有希望的结果.
- 在各种噪音条件下,pDenoiser模型显著提高了语音质量和ASR性能.
- 在多个评估指标上优于现有方法.
结论:
- 在杂的EMS环境中,pDenoiser方法有效地提高了语音清晰度和ASR准确性.
- 这项技术有可能通过减轻非目标语音干扰来提高EMS效率和增强通信安全.
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