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从使用概率贝叶斯神经网络的部表面加速信号估计生理声学特征

Joaquín Sepúlveda1, Jesús A Parra2, Emiro J Ibarra2

  • 1Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.

IEEE transactions on audio, speech, and language processing (2025)
|September 2, 2025
PubMed
概括

这项研究引入了概率贝叶斯神经网络 (PBNN),用于非侵入性语音监测,准确估计语音功能参数和量化预测不确定性. 通过解决异常和认识不确定性,PBNN增强了门诊语音分析.

关键词:
测速器贝叶斯方法贝叶斯网络语音分析语音处理发声

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科学领域:

  • 生物声学与语音科学
  • 计算神经科学
  • 生物医学工程

背景情况:

  • 精确估计声功能参数对于诊断语音障碍至关重要.
  • 无创性门诊语音监控需要强大的方法来处理现实数据的变化.
  • 现有的方法往往难以量化生理参数估计的不确定性.

研究的目的:

  • 开发和验证一个概率贝叶斯神经网络 (PBNN) 来估计关键的声功能变量.
  • 通过解决异常和表观不确定性来增强非侵入性门诊语音监测.
  • 通过使用真实语音数据进行转移学习来完善估计.

主要方法:

  • 一个概率贝叶斯神经网络 (PBNN) 已被开发用于将空气动力学和声学特征与生理参数进行反向映射.
  • 通过使用合成数据的三角形身体覆盖模型 (TBCM) 来训练PBNN.
  • 使用转移学习来整合真实语音数据,完善血压下估计.

主要成果:

  • PBNN成功估计了小球底压力,声接触压力和肌肉激活.
  • 来自PBNN的置信区间与预测错误相关,表明有效的不确定性量化.
  • 在较高的血球下压力下观察到增加的不确定性,这表明捕捉非线性声动态的限制.

结论:

  • PBNN为声功能参数的非侵入性,不确定性意识的估计提供了一个有希望的方法.
  • 该方法通过提供可靠的估计置信区间来增强门诊语音监测.
  • 未来的研究应该探索额外的特征,以更好地捕捉非线性声行为.