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深度学习用于神经肌肉控制声源的声音生产
Anil Palaparthi1, Rishi K Alluri2, Ingo R Titze1
1Utah Center for Vocology, University of Utah, Salt Lake City, UT 84112, USA.
一个深度学习的控制系统准确地管理了发声的肺压力和喉肌肉. 这种计算模型实现了精确的声学和体感目标,这对于语音合成和语音研究至关重要.
科学领域:
- 计算生物物理学的计算生物物理.
- 语音科学是一种语言科学.
- 神经科学是一个神经科学.
背景情况:
- 开发人类声系统的准确计算模型对于理解语音产生至关重要.
- 神经肌肉控制系统在调节声学参数方面发挥着至关重要的作用.
- 以前的模型往往缺乏集成的声学和体感反机制.
研究的目的:
- 开发和评估基于深度学习的语音生成神经肌肉控制系统.
- 整合声学和体感反,以精确控制声学参数.
- 使用LeTalker生物物理模型来模拟和训练控制系统.
主要方法:
- 采用了一个生物物理计算模型 (LeTalker) 与一个三质声折模型.
- 设计了一个深度学习的控制系统,配有声学前和反控制器.
- 该系统是通过LeTalker产生的5万个稳定的语音信号进行训练的.
主要成果:
- 控制系统准确地实现了四个声学目标 (基本频率,声压水平,光谱中位素,信号与噪声比).
- 该系统还准确地满足了四个体感官目标 (声长度,纤维应力).
- 在训练后,反控制器的校正是最小的,除了甲状腺肌肉激活.
结论:
- 开发的深度学习控制系统有效调节肺压力和喉肌肉激活.
- 该模型在实现语音控制的声学和体感目标方面表现出很高的准确性.
- 这种方法为语音生成和声动控制的计算建模提供了一个强大的框架.
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