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.
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.
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.
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