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在光学麦克风系统中通过基于物理的数据增强来提高语音可理解性.

Jia-Wei Chen1, Jia-Hui Li1, Yi-Hao Jiang1

  • 1Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan.

JASA express letters
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此摘要是机器生成的。

这项研究引入了一种数据增强方法,以提高激光多普勒振动计 (LDV) 的语音清晰度. 该技术通过模拟材料扭曲和噪音,以更好地处理音频,从而提高噪音环境中的可理解性.

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科学领域:

  • 声学和信号处理
  • 机器学习用于音频增强
  • 生物医学工程 生物医学工程

背景情况:

  • 激光多普勒振动仪 (LDV) 能够实现非接触式语音获取,但会受到物质依赖的扭曲和斑点噪声的影响.
  • 这些扭曲会显著降低语音可理解性,尤其是在杂的环境中.

研究的目的:

  • 提出一种新的数据增强方法来模拟和减轻LDV引起的语音扭曲.
  • 在各种材料和低信号噪声比 (SNR) 条件下提高LDV录音的语音可理解性.

主要方法:

  • 开发了一种数据增强技术,结合了特定材料和冲动噪声来模拟LDV扭曲.
  • 采用一个封闭的卷积神经网络与HiFi-GAN集成,用于语音增强.

主要成果:

  • 在0dB SNR下,获得了0.76的短期目标可理解性 (STOI) 评分.
  • 在各种材料和低SNR场景中显著改善了语音可理解性.

结论:

  • 拟议的数据增强和深度学习方法有效地提高了LDV录音的语音可理解性.
  • 为优化增强策略和深度学习模型在实际的基于LDV的音频应用程序中提供了宝贵的见解.